Prosiding Seminar Nasional Ilmu Teknik, Its a collection of papers or scientific articles that have been presented at the National Research Conference which is held regularly every two years by the Asosiasi Riset Ilmu Teknik Indonesia. The paper topics published in the Prosiding Seminar Nasional Ilmu Teknik the sub-groups of Civil Engineering and Spatial Planning, Engineering, Electrical and Computer Engineering, Earth and Marine Engineering and other relevant fields and published twice a year (June and December). Prosiding Seminar Nasional Ilmu Teknik published by Asosiasi Riset Ilmu Teknik Indonesia, in collaboration with universities and supported by the Asosiasi Pengelola Jurnal Indonesia. Indexed by: Journal Sponsors and Partners :
Penerapan Metode AHP-Fuzzy untuk Mengukur Pemahaman Penyedia Jasa Konstruksi terhadap Sistem E-Tendering di Kota Surabaya
E-Tendering is an electronic-based procurement method that integrates all parties involved in construction projects through a digital platform, simplifying communication, document exchange, and transactions from the initial stage to project completion. The primary objective of implementing E-Tendering in the construction sector is to improve efficiency and transparency in the procurement process of goods and services. However, construction service providers often face obstacles in E-Tendering, such as limited technological proficiency, administrative errors that may disqualify bids, and technical issues like internet connectivity and platform system reliability. Therefore, this research aims to identify the barriers and level of understanding of E-Tendering among construction service providers in Surabaya. A total of 51 respondents were selected using snowball sampling, based on their participation in the E-Tendering system. Data processing in this study employed a quantitative approach using the Fuzzy AHP method to analyze the criteria and sub-criteria related to understanding and obstacles in participating in electronic construction service tenders (E-Tendering). The results of the analysis of service providers’ understanding of the E-Tendering system using the AHP method showed a λmax value of 9.09, a Consistency Index (CI) of 0.01, and a Consistency Ratio (CR) of 0.007. The CR value, which is far below the threshold of 0.10, indicates that the respondents’ assessments are consistent, meaning that the pairwise comparison results of the nine criteria can be considered valid and reliable.
Copyright (c) 2025 Prosiding Seminar Nasional Ilmu Teknik https://creativecommons.org/licenses/by-sa/4.0
Faktor Utama Penghambat Penerapan Manajemen Risiko Biaya pada Pelaksanaan Proyek Konstruksi Gedung Bertingkat di Surabaya dengan Menggunakan Metode AHP Fuzzy
The implementation of risk management is an important method that is carried out in order to identify risk factors that may arise during the implementation of the project. However, the implementation of risk management still faces some obstacles in its implementation. Therefore, this study aims to analyze the barriers to the implementation of risk management in construction projects in Surabaya. A total of 80 respondents filled out questionnaires from construction service providers including contractors and consultants. Respondents involved include professional experts such as Project Managers, Site engineers, Site managers, implementers, estimators, General Managers and Company Directors. Data processing using fuzzy AHP method as a data processing tool and decision making. The results of the study revealed that the main factors that can hinder the implementation of cost risk management are inaccurate cost estimates (Y4) with the highest weight of 0.433, lack of quality Control (Qc) supervision criteria (Y5) is ranked second with a weight of 0.288, poor coordination between stakeholders (owner,contractor and consultant) (Y1) is ranked third with a weight of 0.274, lack of risk management training (Y3) is ranked fourth with a weight of 0.005, and some, old age) (Y2), the work can not be done according to the work drawings (Y6), limited skilled human resources (Y7), materials not according to specifications (Y8), improper initial cost estimation (Y9), late progressive payment from the owner (Y10) ranked fifth jointly because it has an equivalent weight value of 0.These findings conclude that accurate cost estimation (Y4) is very important in construction projects because it becomes the main basis in budget planning, decision making, and risk management.
Faktor Dominan Keterlambatan pada Proyek Konstruksi Gedung Bertingkat di Surabaya dengan Metode Relative Importance Index (RII)
Delays in high-rise building construction projects in Surabaya are a crucial issue as they can significantly affect cost, quality, and project completion schedules. This reserach intends to identify the dominant factors contributing to project delays using the Relative Importance Index (RII) method, considering both frequency and impact aspects. Data were obtained from 80 valid respondents who hold strategic roles in construction project implementation, such as Project Managers, Site Managers, Site Engineers, Estimators, Field Supervisors, General Managers, and Company Directors. The analysis results indicate that in terms of frequency, the dominant factors are unfavorable weather conditions (RII = 0.750), labor shortages (RII = 0.743), and design changes during construction (RII = 0.735). Meanwhile, in terms of impact, the dominant factors are delays in material delivery (RII = 0.780), labor shortages (RII = 0.770), and substandard material quality (RII = 0.740). These findings emphasize that labor shortage consistently influences project delays in both frequency and impact, while weather and material factors show different patterns between occurrence and consequence. All dominant factors were classified into the High-Medium category, indicating that they should be prioritized in project risk management.
The Influence of Work Environment and Work Culture on the Fatigue of the MT. Sultan Mahmud Badaruddin II Crew
This research is motivated by the high risk of fatigue experienced by ship crews during voyages, which directly affects occupational safety and mental well-being. Fatigue arises from long working hours, inadequate rest time, heavy workloads, and extreme environmental conditions such as high temperatures, engine noise, and vessel vibrations. On the MT. Sultan Mahmud Badaruddin II, the problem becomes more complex due to the tight work rhythm, short berthing periods, and fast, repetitive loading–unloading activities. Harsh weather conditions, short but intensive sailing distances, and limited relaxation facilities make the crew increasingly vulnerable to both physical and mental fatigue. In addition, a work culture that tends to be authoritarian and lacks communication exacerbates psychological pressure, especially when crew members find it difficult to report their fatigue to superiors. This study uses a qualitative method through direct observation and interviews with all crew members in the deck and engine departments. The aim is to analyze the influence of the work environment and work culture on fatigue levels onboard. The results show that environmental factors such as high temperatures, narrow workspaces, and vessel instability significantly affect physical fatigue. Meanwhile, mental fatigue is triggered by ineffective communication, hierarchical pressure, and an unsupportive work culture. These findings align with the perspectives of Mathis and Jackson and comply with the provisions of the STCW 2010 and MLC 2006, which emphasize the importance of regulating working hours and fatigue management. Overall, optimizing rest hours, improving the work environment, and reforming organizational culture are required to reduce fatigue risks.
Analysis of the Effectiveness of Technical Personnel Management on Main Engine Damage During Extreme Weather Conditions at Sea
This study aims to analyze the effectiveness of technical personnel management in handling main engine failures during extreme weather conditions at sea. The main focus of this study is to assess the extent to which technical competence, communication, coordination, and preparedness of technical personnel contribute to the effectiveness of damage management. The method used is a descriptive quantitative approach with data collection through a closed-ended questionnaire based on a Likert scale. A total of 100 respondents who are ship engineering officers currently studying at a maritime campus were sampled. The results of the analysis show that the four independent variables (technical competence, communication, coordination, and preparedness) simultaneously have a significant effect on the effectiveness of handling main engine failures. From the results of the multiple linear regression test, the coefficient of determination (R²) value of 0.897 indicates that 89.7% of the variation in damage management effectiveness can be explained by these four variables. This finding indicates that good technical personnel management plays a significant role in reducing the risk of engine system failure during extreme weather.
Sistem Informasi Inventory Berbasis Website Pada Toko Anugerah Sticker di Semarang
This study aims to develop a web-based inventory information system to facilitate inventory data management and improve operational efficiency at Toko Anugerah Sticker. Currently, inventory management is still performed manually using paper, which complicates monitoring stock, recording incoming and outgoing items, and preparing reports accurately and promptly. To address these issues, the system was developed using the Waterfall method, consisting of five stages: requirement, design, implementation, verification, and maintenance. PHP was employed as the programming language for system development, while MySQL served as the database for storing inventory data. The system was tested using Blackbox testing to unsure that each function worked as expected. The result show that the web-based inventory information system successfully replaced the manual process, making inventory management more effective and efficient. It also enables real-time stock monitoring and generates reports faster and more accurately. The implementation of this system is expected to improve the quality of inventory data management at Toko Anugerah Sticker in the future.
Copyright (c) 2026 Prosiding Seminar Nasional Ilmu Teknik https://creativecommons.org/licenses/by-sa/4.0
Implementasi MADM Dalam Sistem Pendukung Keputusan Pemilihan Kandidat Magang Social Media Specialist Menggunakan AHP-TOPSIS Pada PT.XYZ
This research aims to implement a Multi-Attribute Decision Making (MADM) approach using the AHP–TOPSIS method to assist PT. XYZ in selecting the most suitable intern candidate for the Social Media Specialist position. The increasing number of applicants each year makes the selection process more complex, requiring a systematic and data-driven decision support system. AHP was used to determine the priority weights of five main criteria—Interview, Experience, Portfolio, Skill, and Achievement—along with their subcriteria. All pairwise comparison matrices met the consistency requirements, indicating valid weights. The TOPSIS method was then applied to calculate the preference scores of ten candidate alternatives based on the weighted normalized decision matrix, ideal solutions, and distance measures. The results show that candidate A3 achieved the highest preference score (0.9422), followed by A7 and A8, making them the top recommended candidates. This study demonstrates that integrating AHP and TOPSIS effectively supports companies in conducting objective, efficient, and accurate recruitment decision-making processes.
Peningkatan Kapasitas Masyarakat dalam Mitigasi Banjir Melalui Pelatihan dan Penerapan Rumah Amfibi di Wilayah Rawan Bencana Kalimantan Tengah
This study examines community capacity building for flood mitigation in flood-prone regions of Central Kalimantan through disaster education, technical training, and the construction of an amphibious house prototype. Using a community-based disaster risk reduction (CBDRR) framework, the program integrates participatory training, field surveys, and adaptive structural innovation. Findings indicate a 40% increase in community knowledge based on Community Empowerment Level Analysis results, active engagement of 35 participants in disaster education, and significant improvement in technical skills among 22 trainees involved in amphibious foundation construction. The prototype achieved 100% completion within four effective working days, demonstrating the feasibility of amphibious technology using locally available materials. Strengthening youth organization structures further enhances community readiness and institutional resilience. Overall, the integration of participatory learning and adaptive technology effectively builds community self-efficacy and disaster preparedness in flood-prone environments.
Optimasi Konsentrat Ransum Pakan Sapi Perah Laktasi Menggunakan Whale Optimization Algorithm
This study is based on the need to develop a more effective concentrate ration for lactating dairy cows, as existing formulations in the field are greatly influenced by the availability of ingredients and varying quality. Therefore, this study focuses on optimizing concentrate in dairy cow feed rations to meet SNI standards, which include crude protein (CP), Total Digestible Nutrients (TDN), Calcium (Ca), and Phosphorus (P), with more efficient results in terms of price and nutrition. This study uses the Whale Optimization Algorithm (WOA) metaheuristic approach, which balances the exploration and exploitation processes in finding the best solution to optimization problems. This algorithm has fewer parameters than other metaheuristics such as GA, PSO, and DE. WOA runs naturally in continuous space without the need for genetic operators such as crossover and mutation. The dataset used contains types of dairy cow feed ingredients along with nutritional requirements and prices so that researchers can process the data into efficient feed concentrate that is suitable for lactating dairy cows.
Rancang Bangun Model Segmentasi Semantik U-Net untuk Monitoring Lingkungan Tambang Berbasis Citra Satelit Sentinel-2
Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.
Rancang Bangun Media Ajar Iqro’ Jilid 5 Interaktif Berbasis Teknologi Augmented Realty
Learning to read the Al-Qur'an at TPA Al-Amin Brahu Ponorogo still relies on conventional methods, which lead to low motivation and boredom among students. This study aims to design and develop interactive learning media based on Augmented Reality (AR) through the AR-Iqro' Jilid 5 application on the Android platform. The development method employed is the System Development Life Cycle (SDLC) using the Waterfall model, which encompasses the stages of planning, design, implementation, testing, and maintenance.The results of the study indicate that the application performs exceptionally well, with material validation reaching 96%, media design at 96%, and user testing at 97%. These findings prove that the AR-Iqro' Jilid 5 application is highly feasible for use due to its ease of navigation and intuitive visual interface. The implication of this research is the availability of an innovative alternative learning medium capable of increasing students' interest in learning the Al-Qur'an, with the potential for broader implementation in technology-based Islamic educational institutions.
Sentiment Analysis of YouTube Comments on Indonesia’s Performance in 2026 World Cup Qualifiers Using Naive Bayes
Sentiment analysis on social media is widely used to represent public perceptions of sports performance, particularly in international competitions. This study aims to analyze the sentiment of YouTube user comments regarding the performance of the Indonesian National Football Team during the FIFA World Cup 2026 Asian Qualifiers. The data were collected from user comments on videos related to the matches and analyzed using a machine learning–based sentiment analysis approach. Sentiment classification was performed using the Naive Bayes algorithm. The results indicate that the proposed approach is able to effectively identify public sentiment toward the national team’s performance during the qualification matches. The findings of this study are expected to provide insights into public perceptions and contribute to sentiment analysis research in the field of sports.
Klasifikasi Arah Pengendali Karakter Permainan Berbasis Gestur Badan Menggunakan Hybrid Convolutional Neural Network dan Long Short-Term Memory
The increasing sedentary lifestyle in the digital era has the potential to cause various health problems due to lack of physical activity. One approach that can be taken to encourage physical activity is through the use of digital games with body movement-based control mechanisms. This study aims to develop a body gesture-based game character control system using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. CNN is used to extract spatial features from each video frame, while LSTM serves to model the temporal relationship between frames so that movement patterns can be recognized sequentially. The research method used refers to the Machine Learning Lifecycle stages, starting from data collection, preprocessing, model development, to implementation in the endless runner game genre. Testing results show that the CNN–LSTM model is capable of classifying body gestures and generating outputs that can be used as commands to control game characters. The implementation of this system enables more natural and interactive game interactions without conventional input devices, and has the potential to encourage players to lead a more active lifestyle.
Perancangan Sistem Informasi Monitoring Data KWH Meter Menggunakan Metode User Centered Design (UCD)
The management of kWh meter replacement data at PLN ULP Ampera Palembang is still largely handled through manual recording, which often causes data inconsistencies and delays in monitoring activities. This study aims to design an information system that supports the monitoring of kWh meter replacement data based on actual user needs. The research applies a descriptive qualitative method using the User Centered Design (UCD) approach, where users are actively involved throughout the design process. The stages include understanding the work context, identifying user requirements, developing system design solutions, and evaluating the proposed design. The outcome of this research is a kWh meter data monitoring system design that is expected to facilitate data management, improve accuracy, and support more efficient monitoring processes.
Analisis Sentimen Publik pada TikTok terhadap Rencana Penerapan Sistem Balik Nama Ponsel Bekas menggunakan Naive Bayes dan Support Vector Machine
Since the announcement of the policy plan for a name transfer system in the sale of used mobile phones, the issue has attracted widespread public attention and discussion. People have expressed their opinions on social media platforms, particularly TikTok. This study aims to classify the sentiment of TikTok users using Naive Bayes and Support Vector Machine (SVM) algorithms. The data were collected through a comment scraping technique on related content.The research stages include text preprocessing, sentiment labeling into positive, negative, and neutral categories, and feature extraction using TF-IDF. The classification process employs Naive Bayes and Support Vector Machine algorithms, which are then evaluated based on accuracy, precision, recall, and F1-score. The results of this study indicate that both methods are capable of classifying sentiment effectively. However, the Support Vector Machine method is superior to the Naive Bayes method with an accuracy rate of 99.57% compared to 94.30%. This study is expected to help the government understand public responses to the planned policy of the used mobile phone name transfer system.
Pengembangan Sistem Informasi Persewaan Mesin Vacuum Cleaner Berbasis Web Dengan Metode MVC Pada Toko Akasa Teknik
Pemanfaatan teknologi informasi menjadi kebutuhan penting bagi usaha jasa persewaan untuk meningkatkan efisiensi dan kualitas layanan. Toko Akasa Teknik sebagai usaha persewaan mesin vacuum cleaner masih menghadapi permasalahan dalam pengelolaan data penyewaan, transaksi, dan pelaporan yang dilakukan secara manual sehingga berpotensi menimbulkan kesalahan pencatatan dan keterlambatan informasi. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem informasi persewaan berbasis web yang mampu mengintegrasikan seluruh proses bisnis secara terpusat. Metode pengembangan sistem yang digunakan adalah model Waterfall yang meliputi tahap analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Sistem dikembangkan menggunakan bahasa pemrograman PHP dengan arsitektur Model–View–Controller (MVC) dan basis data MySQL. Pengujian dilakukan menggunakan metode Black Box Testing untuk memastikan setiap fungsi berjalan sesuai dengan kebutuhan pengguna. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu meningkatkan efisiensi pengelolaan data, mempercepat proses penyewaan, serta memudahkan pelanggan dalam melakukan pemesanan dan memantau status sewa. Dengan demikian, sistem informasi ini diharapkan dapat meningkatkan kualitas layanan serta mendukung daya saing Toko Akasa Teknik di era digital.
Desain Chatbot Digital Twin Atlet Pencak Silat
This research aims to develop a customized chatbot based on a local large language model (LLM) using Ollama Anything as a form of psychosocial support for Pencak Silat athletes. Mental toughness is a critical factor for Pencak Silat athletes, particularly when coping with competitive failure or sports-related injuries. Injuries sustained in Pencak Silat competitions often involve psychological consequences, including trauma, fear, anxiety, and disturbances in self-identity. To address these challenges, the proposed chatbot functions as a screen-integrated psychosocial support system for athletes. This research used an experimental method combined with Natural Language Processing (NLP) techniques was employed to construct a digital twin chatbot capable of simulating athlete-centered conversations. The Pencak Silat Athlete Chatbot is designed to assist athletes by providing responsive support when they experience defeat or performance setbacks during competitions. The research findings indicate that, although the chatbot is functional, its conversational responses remain relatively rigid, access times are prolonged, and further testing with Pencak Silat athletes in controlled settings is required. Overall, the development of the Pencak Silat Athlete Digital Twin Chatbot represents an ongoing effort to advance digital innovation and strengthen the ecosystem of sports achivements development in Indonesia.
Klasifikasi Berita Hoaks Menggunakan Algoritma Support Vector Machine
The spread of hoax news in digital media is a serious problem because it can affect public opinion and social stability. This study aims to classify hoax news using the Support Vector Machine (SVM) algorithm. The dataset used is a hoax clarification dataset from the Ministry of Communication and Digital (Komdigi) of the Republic of Indonesia, totaling 1,872 data. The research process includes data collection, text pre-processing, feature extraction using TF-IDF, and classification using the SVM algorithm. Implementation was carried out using Google Colaboratory (Google Colab). Test results show that the SVM algorithm is able to provide good performance in classifying hoax news based on its topic with satisfactory accuracy, precision, recall, and F1-score values.
Model Machine Learning untuk Klasifikasi Loyalitas Pelanggan Menggunakan Random Forest
This research develops a machine learning model to classify customer loyalty using the Random Forest algorithm. Customer churn is a critical issue that reduces revenue and increases acquisition costs. A dataset of 50,000 customers from global e-commerce and subscription platforms was processed through data cleaning, imputation, outlier handling, and class balancing with SMOTE. The Random Forest model was built as a baseline and optimized with hyperparameter tuning. Evaluation using accuracy, precision, recall, and F1-score shows that the optimized model achieved 90.81% accuracy and 83.87% F1-score, outperforming previous Naïve Bayes approaches. Feature importance analysis highlights customer service interactions, lifetime value, and demographic factors as key predictors of churn. These findings demonstrate Random Forest’s effectiveness in churn prediction and provide practical insights for customer retention strategies
Reflectai: Otomatisasi Perangkat Pembelajaran Mendalam Menggunakan Machine Learning dan Student Behavior Analysis Melalui LMS
This study presents ReflectAI, a web-based system designed to automate the creation of teaching materials tailored to students' learning styles using behavior data from a Learning Management System (LMS). Student digital activity data—such as logins, material access, forum participation, assignment submission, and quiz results—are extracted and processed using a Hierarchical Clustering algorithm to categorize students into three learning styles: visual, auditory, and kinesthetic. Based on the clustering results, the system automatically generates personalized learning modules using generative AI (ChatGPT API), aligned with each student's learning preferences. Employing a data-driven system development approach, the system was tested with data from 230 students in a mathematics course. The results show diverse learning style distributions and relevant, tailored content generation. ReflectAI is designed to reduce teachers’ administrative workload and enhance personalized and adaptive learning. This system contributes to educational transformation through deep, data-driven technology integration.
Aplikasi Computer Vision pada Desain Batik Folklor Muara Jambi sebagai Upaya Inovasi Digital Berkelanjutan
This research aims to recontextualize the local wisdom embedded in the visual culture of the Muara Jambi Temple through the development of folklore-inspired batik motif designs. As a traditional Indonesian textile craft, batik serves not only functional purposes but alsp embodies profound cultural values and indigenous knowledge systems. Batik as a medium of cultural expression that invites multidimensional interpretaion within both practical and theoretical academic discourses. Its visual strength lies in the richness of colors, ornaments, and symbolic elements, which generate diverse interpretative meanings. These interpretations subsequently shape value systems that function as guiding principles in the everyday lives of Indonesian communities. The research used an experimental design method integrated with computer vision techniques to generate distinctive folklore batik motifs rooted in the cultural heritage of Muara Jambi, Sumatra. The research results are the creation of a Muara Jambi folklore batik motif that represents cultural expression, resilience, and continuity through the preservation of traditional patterns and environmentally conscious practices within the Jambi community. The transformation of motifs derived from artifacts from the Muara Jambi temple complex, serves as a primary source of inspiration for contemporary folklore batik design. The application of computer vision in the batik design process constitutes a form of sustainable digital innovation, facilitating the preservation, reinterpretation, and adaptive transformation of traditional visual heritage.
Komparasi Algoritma SVM dan Random Forest Dalam Sentimen Analisis Review Shopee di Google Play Store Dengan Anova
The rapid growth of e-commerce in Indonesia, particularly the Shopee platform, has generated a large volume of user reviews on the Google Play Store, which can be analyzed to understand consumer sentiment. This study aims to compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in binary sentiment classification (positive and negative) on Shopee reviews, as well as to statistically test the significance of their differences using One-Way ANOVA. A total of 400,498 reviews were collected via web scraping, preprocessed through text normalization, tokenization, and Indonesian language stemming, and then feature-extracted using TF-IDF and Count Vectorizer. Evaluation results show that SVM achieved an accuracy of 91.77%, precision of 91.49%, recall of 91.77%, and F1-Score of 91.56%, while RF achieved an accuracy of 90.07%, precision of 91.68%, recall of 90.07%, and F1-Score of 90.55%. ANOVA confirmed that the performance difference between the two algorithms is statistically significant (p-value = 0.0007) with a large effect size (η² = 0.1815). Therefore, SVM is recommended as a more optimal and consistent algorithm for automated sentiment analysis of Indonesian e-commerce reviews, while also providing a replicable methodological framework for similar future research.
Optimasi XGBoost Dengan SHAP Untuk Sistem Skrining Penyakit Jantung
Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.
Sistem Pendukung Keputusan Eligible Seleksi Nasional Berdasarkan Prestasi (SNBP) Menggunakan Metode Additive Ratio Assessment (ARAS)
The selection process for students eligible for the National Selection Based on Achievement (SNBP) requires objective and structured assessment because it involves various academic and non-academic criteria. This study aims to develop a Decision Support System (DSS) to determine the ranking of SNBP eligible students at SMAN 8 Surabaya using the Additive Ratio Assessment (ARAS) method. The ARAS method is used to evaluate student alternatives based on their report card scores for semesters 1-5, academic ability tests (TKA), academic achievements, non-academic achievements, discipline, organizational activity, and attendance through a normalization process to obtain relative Ki values. The results of the study show that the system is capable of producing objective student rankings with relative utility values (Ki) ranging from 95.15 to 89.38, where the highest value indicates the best alternative from all alternatives. The application of ARAS-based DSS can improve the efficiency, transparency, and consistency of the SNBP student selection process.
Perancangan Alat Deteksi Tingkat Kematangan Buah Mangga Indramayu Berdasarkan Kandungan Gas dan Pengolahan Citra Menggunakan YOLOv11
Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.
Pencegahan Konflik Berbasis Smart System di Kawasan Transmigrasi Momi Waren, Ransiki, dan Oransbari Kabupaten Manokwari Selatan
Conflicts in transmigration areas are generally multidimensional and influenced by social, economic, land, and institutional factors. This study aims to identify the forms and distribution of conflicts in three districts of the transmigration area, namely Momi Waren District, Ransiki District, and Oransbari District, as well as to formulate a smart system-based conflict resolution approach through the use of spatial data, local institutions, and local wisdom-based settlement practices. Based on field mapping, four main categories of conflict were identified: 1) Land conflicts occur throughout the transmigration sites in the form of claims to transmigration land that has not been handed over to transmigrants because the compensation price is below normal. In addition, there is no ATR BPN office in South Manokwari Regency, one of whose functions is community empowerment and conflict resolution. 2) Economic conflicts occur because transmigrants are registered and recorded in the population registry, making it easy for them to access capital. Several economic activities in agriculture and transportation services are dominated by transmigrants, causing economic jealousy. 3) Social conflicts occur when the distribution of social assistance is uneven and the excessive use of illegally sold alcoholic beverages causes social unrest. 4) Institutional conflicts occur when civil servants, police, and military personnel are recruited, and not all indigenous Papuans who are nominated can be accommodated, requiring the involvement of tribal councils to formulate recommendations for recruitment that prioritize indigenous Papuans. The root causes of the conflict were analyzed using a root cause analysis approach that covered unclear land boundaries, unequal economic access, weak coordination between institutions, and low social trust due to differences in interests between groups. This study utilizes best practices from the Tribal Council, the South Manokwari Regency Transmigration and Manpower Office, the Religious Harmony Forum, and the Social Services Office as the basis for developing smart maps for an early warning system for conflicts. The results of the study formulate a Smart Conflict Resolution System framework consisting of three main components: (1) participatory spatial mapping of conflicts and key actors, (2) integration of institutional databases and social-customary mediation channels, and (3) design of smart maps as a mitigation and decision-making tool in transmigration areas. This system is expected to strengthen collaborative governance, prevent conflict escalation, and realize inclusive and sustainable management of transmigration areas
Pengukuran Tingkat Kesiapan Pasien RSUD Abdul Manap Terhadap Mobile JKN Menggunakan Metode HOT-Fit
The digital transformation of health services through the Mobile JKN application was introduced by BPJS Kesehatan to provide easier access for the public in obtaining information, managing membership administration, and receiving health services more quickly and efficiently. This study aims to measure the readiness level of patients at Abdul Manap Regional Hospital, Jambi City, in adopting the Mobile JKN application using the HOT-Fit method, which covers three main components: Human, Organization, and Technology. Data were collected from 360 respondents through questionnaires and analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM). The results indicate that technology factors—including system quality, information quality, and service quality— along with organizational support have a significant effect on system use and user satisfaction, which in turn positively influence the net benefits. The outer loading values of all indicators exceeded 0.7, with Composite Reliability above 0.8 and AVE above 0.6, confirming that the research instruments are valid and reliable. Overall, patients at Abdul Manap Hospital are categorized as ready to adopt Mobile JKN, although improvements in digital literacy and stronger organizational support are still required to optimize its utilization.
Segmentasi Pelanggan Ritel Global dan Inggris Menggunakan RFM dan K-Means Clustering
Transaction logs in online retail provide opportunities for data-driven customer segmentation. This study segments customers at two scopes global (all countries) and United Kingdom (UK) using Recency, Frequency, and Monetary (RFM) features derived from the Online Retail transaction dataset. After cleaning cancellations and invalid records, RFM variables are computed per customer and normalized. K-Means clustering is applied separately for global and UK data, while the number of clusters is selected via the elbow criterion and validated using internal indices. The best configuration for both scopes yields five clusters, with moderate separation quality based on the silhouette score. Cluster profiling indicates distinct groups ranging from low-frequency low-spending customers to highly frequent high-spending customers. The comparison between global and UK segmentation shows similar structural patterns, yet different proportions across segments, supporting targeted retention and value-driven marketing actions.
Evolusi Performa Arsitektur Deep Learning melalui Optimasi Bertahap dan Interpretabilitas Grad-CAM untuk Klasifikasi Penyakit Ikan Air Tawar
Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.
Implementasi Simple Additive Weighting Untuk Rekomendasi Karir Mahasiswa Berdasarkan Hasil Tes Psikologi
Career selection is an important process for students at Darussalam Gontor University (UNIDA) because it influences their academic development and future employment. However, many UNIDA students experience difficulties in determining suitable careers due to a lack of understanding of their psychological characteristics. This study aims to build a Decision Support System (DSS) for career recommendations for UNIDA students based on psychological test results using the Simple Additive Weighting (SAW) method. The psychological data used are non-clinical test results collected through a structured questionnaire from six respondents and converted into numerical scores. The research stages include determining criteria and weights, compiling a decision matrix, normalization process, calculating preference values, and ranking career alternatives using SAW. The career alternatives used consist of academics, corporate professionals, entrepreneurs, managers, and social/public services. The results show that the managerial career alternative obtained the highest preference value of 0.861, followed by entrepreneurs at 0.824, corporate professionals at 0.778, social/public services at 0.737, and academics at 0.703. These findings demonstrate that the SAW method is capable of providing objective and systematic career recommendations based on the psychological profiles of UNIDA students. This research is expected to assist UNIDA students and academics in making more informed career decisions tailored to individual characteristics
Pengolahan Citra Digital Kamera Multispektral Berbasis Drone dengan Artificial Neural Network (ANN) untuk Identifikasi Cekaman Air pada Tanaman Padi
Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.
Analisis Korelasi Faktor Gaya Hidup Terhadap Indikator Kesehatan Menggunakan Metode Shapiro-Wilk dan Spearman
Lifestyle changes, such as smoking habits, low physical activity, and suboptimal sleep patterns, have the potential to affect various health indicators. However, empirical evidence showing a direct link between lifestyle factors and objective health indicators still shows varying results. This study aims to analyze the relationship between lifestyle factors and health indicators in adult respondents, in an effort to provide an empirical picture of behavioral factors related to health conditions. This study used a quantitative approach with a cross-sectional observational analytic design. Data were obtained from 94 respondents who had complete lifestyle data and health examination results. Lifestyle factors analyzed included smoking habits, exercise frequency, and sleep duration, while health indicators included body mass index (BMI), systolic and diastolic blood pressure, blood sugar levels, cholesterol, uric acid, and pulse rate. Data analysis was performed using descriptive statistics, the Shapiro–Wilk normality test, and the Spearman correlation test according to the characteristics of the data distribution. The results showed that smoking habits were significantly associated with diastolic blood pressure (p < 0.05), exercise frequency was significantly associated with BMI and systolic blood pressure (p < 0.05), and sleep duration was significantly associated with uric acid levels (p < 0.05). Meanwhile, the relationship between lifestyle factors and other health indicators did not show statistical significance. These findings indicate that the influence of lifestyle on health is specific to certain indicators and is not evenly distributed across all health parameters. This study concludes that identifying lifestyle factors relevant to certain health indicators is important as a basis for formulating more targeted health promotion strategies.
Analisis Tingkat Kepuasan Pengguna Pada Website Smp Negeri 5 Kota Jambi Menggunakan Model Webqual 4.0 Dan Importance Performance Analisis (Ipa)
Advances in information technology have changed the way humans obtain and manage information, including in the world of education. School websites have become an important medium for conveying academic, administrative, and school activity information quickly and efficiently. However, not all educational institutions are able to optimize the functions of their websites. This study was conducted to determine user satisfaction with the website of SMP Negeri 5 Kota Jambi using the Webqual 4.0 model and Importance Performance Analysis (IPA). The research method used was a descriptive quantitative approach with data collection through the distribution of questionnaires to 291 respondents from a total population of 1,065 students. The analysis was conducted by measuring the three main dimensions of Webqual 4.0, namely usability quality, information quality, and service interaction quality, then using IPA to map service improvement priorities. The results showed that most users were satisfied with the quality of the website, particularly in terms of ease of use and service interaction. However, the timeliness of information updates and the responsiveness of the display on mobile devices still needed improvement. Recommendations for improvement focused on the dimensions in the "Concentrate Here" quadrant of the IPA analysis.
Analisis Performa Convolutional Neural Network untuk Klasifikasi Aktivitas Pengemudi Terganggu
Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.
Evaluasi Kinerja Model Klasifikasi Machine Learning untuk Deteksi Fraud pada Data Transaksi Tidak Seimbang
Evaluasi Tingkat Kematangan Tata Kelola Sistem Informasi pada Perpustakaan UNAMA di Kota Jambi Berdasarkan Framework COBIT 2019 (Domain DSS)
The UNAMA Library employs information systems to enhance its academic services. Nevertheless, its administrative framework continues to encounter obstacles, such as inadequate service system support for users, constrained resources allocated for the management and upkeep of the system, an absence of standardized protocols for addressing technical challenges, and insufficient assessment system efficacy. If these issues remain unaddressed, the operational effectiveness of the library information system will be compromised, thereby thwarting the objective of delivering dependable information services. This investigation seeks to illuminate the maturity level of information system governance as delineated by COBIT 2019 within the Decision Support Systems (DSS) domain, specifically focusing on the DSS01 (Manage Operations) and DSS02 (Manage Service Requests and Incidents) processes. The findings suggest that the degree of information system governance capability the UNAMA Library is situated at the Established Process level (level 3), signifying that the process has undergone implementation; however, it has yet to be comprehensively documented and consistently evaluated. Moreover, a disparity persists between the existing state and the anticipated capability level of the organization, particularly concerning IT operations management, the standardization incident handling, and the documentation of operational procedures. An elucidation of the expected level is articulated, especially in terms of operational standards, incident documentation, and IT infrastructure oversight. Recommendations encompass the formulation of standard operating procedures (SOPs), the enhancement of documentation practices, and periodic assessments grounded in COBIT 2019. These findings are anticipated to assist libraries in augmenting the efficacy of information systems governance and the quality of IT services.
Perbandingan Akurasi BERT dan RNN pada Analisis Sentimen Komentar Hotel
The increasing volume of online hotel reviews provides valuable insights into customer perceptions but poses challenges for manual analysis due to its unstructured nature. This study aims to compare the performance of Recurrent Neural Network (RNN) and Bidirectional Encoder Representations from Transformers (BERT) in hotel review sentiment analysis. A total of 20,491 TripAdvisor hotel reviews were classified into three sentiment categories: negative, neutral, and positive. The research methodology includes text preprocessing, stratified data splitting, class imbalance handling using Random Over-Sampling, tokenization, and supervised model training. Model performance was evaluated using a confusion matrix and classification metrics. The results indicate that BERT outperforms RNN, achieving an accuracy of 80.54%, while RNN reached 62.21%. BERT demonstrated superior capability in capturing contextual and semantic information in hotel reviews. These findings suggest that transformer-based models are more effective for sentiment analysis of complex textual data in the hospitality domain and can support data-driven service improvement strategies.
Klasifikasi Penggunaan Daya Listrik Rumah Tangga dengan Menggunakan Metode Naive Bayes
Data mining is a technique of several fields of science to find previously unknown relationships in the data warehouse so that it becomes an information that can be used later. The unwise use of electricity will of course have an impact on the high use of electricity, therefore it is expected that every community understands the effort to use electricity wisely. Therefore, authors perform analysis of data mining on these electrical usage data in order to know which is a small, medium and large category. The authors use data on electrical use questionnaire as much as 200 data which is then presented into the ARFF format. In performing author analysis using WEKA Tools. The method used is Naive Bayes classification method with the greatest percentage of accuracy obtained using the Use Training Set Correctly of 80.5%, using a 5-Fold Cross Validation Correctly of 75%, and using 10-Fold Cross Validation amounted to 74%. While the result of the selection of the attributes using the algorithm classifier attribute evaluation (ClassifierAttributeEval) is stated that the most influential attribute against the electrical power usage classification is Electonic Goods.
Evaluasi Kinerja Machine Learning pada Klasifikasi Penyakit Jantung Menggunakan Teknik Penyeimbangan Data
AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.
Analisis Kepuasan Pengguna Aplikasi Dramabox Pada Kalangan Generasi Z Kota Jambi Menggunakan Metode End User Computing Satisfaction (EUCS)
The development of video streaming services has made DramaBox widely used by Generation Z, who prioritize fast entertainment access through mobile devices. However, complaints such as long ad duration, unstable video quality, and inaccurate subtitles remain obstacles that can reduce user satisfaction. This study analyzes the factors that influence DramaBox user satisfaction in Jambi City using the End User Computing Satisfaction (EUCS) method with five main dimensions: content, accuracy, format, ease of use, and timeliness. Data were obtained through questionnaires completed by 385 respondents and processed using SEM-PLS with the help of SmartPLS 4. The results showed that accuracy and timeliness significantly influence user satisfaction, while content, format, and ease of use did not have a significant impact. This finding indicates that information reliability and system speed are the most determining aspects of user experience. Therefore, improvements in both aspects are important for the future development of the DramaBox application.
Deteksi Risiko Diabetes Pada Wanita Hamil Menggunakan Algoritma Random Forest
Gestational Diabetes Mellitus (GDM) is a pregnancy-related metabolic disorder that poses health risks to both mother and fetus if not detected early, requiring accurate prediction methods for early screening and clinical decision-making. This study applies the Random Forest algorithm to detect GDM risk using clinical data from the Pima Indian Dataset. Data preprocessing included handling missing values, standardization, feature engineering, and a 70:30 train–test split. Two models were developed: a baseline and an optimized model using GridSearchCV hyperparameter tuning, validated with 5-fold cross-validation. Performance was assessed using a classification report, confusion matrix, and ROC–AUC. Results show that the optimized model outperforms the baseline, achieving 88% accuracy, an AUC of 93%, and average recall of 81%–85%. Compared to previous studies, this approach demonstrates improved predictive performance. The findings indicate that combining Random Forest with comprehensive preprocessing, feature engineering, and model optimization is effective and feasible for developing a medical decision support system for early GDM risk screening.
Analisis Kepuasan Pengguna Aplikasi Melolo Menggunakan Metode End User Computing Satisfaction (EUCS)
The development of digital technology has led to an increase in the use of short video-based entertainment applications, including the Melolo application. However, the free version still has various complaints, such as inconsistent subtitles, unintuitive navigation, force close glitches, and unstable advertisements, so user satisfaction analysis is needed. This study aims to measure the level of satisfaction of users of the free version of the Melolo application using the End User Computing Satisfaction (EUCS) method, which covers five variables, namely content, accuracy, format, ease of use, and timeliness. Data was collected through an online questionnaire of 385 Melolo app users in Jambi City and analyzed using Structural Equation Modeling (SEM) with the help of SmartPLS 4. The results showed an R-Square value of 0.546, indicating that the model was able to explain 54.6% of the changes in user satisfaction levels. The variables of content and timeliness were found to have a significant effect on user satisfaction, while accuracy, format, and ease of use had no significant effect. These results indicate that content quality and system timeliness are the main factors in increasing user satisfaction. Therefore, Melolo app developers are advised to maintain content quality and improve system performance and stability to optimize the user experience.
Analisis Sentimen Ulasan Penggunaan Aplikasi Maxim Pada Google Play Store Menggunakan Algoritma Naive Bayes, SVM, CatBoost Berbasis NLP
Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify user opinion tendencies based on textual reviews. This study analyzer user reviews of the Maxim application on the Google Play Store and compares three Machine Learning algoritmhs-Naïve Bayes, Support Vector Machine (SVM), and CatBoost-in classifying sentiment. The research stages include data collection, text preprocessing, feature extraction using TF-IDF and Chi-Square, class balancing using SMOTE, and performance evaluation through Accuracy, Precision, Recall, and F1-Score. ANOVA is used to examine the influence of feature selection on model performance. The results show that each model exhibits different performance level across the tested feature combinations. The CatBoost achieved the highest accuracy of 99,26% and demonstrating the most stable performance. Meanwhile, the Naïve Bayes and SVM models experienced performance decreases experiments, especially after applying SMOTE. These findings indicate that the choise of algorithm, feature extraction method, and class balancing technique significantly affects classification outcomes. Overall, CatBoost is identified as the best-performing model, providing more consistenst classification result in accordance with the characteristics of the user reviews.
Implementasi Data Mining dengan Teknik Smote dan Fitur Gain Ratio Untuk Klasifikasi Kelayakan Siswa Penerima PIP di Kota Jambi
The Indonesia Pintar Program (PIP) is an educational assistance program for students from underprivileged families, but determining the eligibility of recipients still faces obstacles in the form of subjectivity and data imbalance. This study aims to classify the eligibility of high school students receiving PIP in Jambi City using data mining methods. The SMOTE technique was applied to overcome class imbalance, and Gain Ratio feature selection was used to determine important attributes. The dataset used consisted of 19,596 student data with a training data distribution of 70% and testing data of 30%. The classification process used the Naïve Bayes, Decision Tree (J48), and Random Forest algorithms with the Use Training Set, 5-Fold, and 10-Fold Cross Validation testing schemes. The results show that SMOTE improves model performance, but feature selection in some cases reduces accuracy. Overall, Random Forest without feature selection provides the best results with an accuracy of 93.33% and is recommended as the most effective model for objectively determining PIP recipient eligibility.
Analisis Kepuasan Pengguna Aplikasi WPS Office Menggunakan Metode End User Computing Satisfaction (EUCS)
WPS Office stands for Writer, Presentation, and Spreadsheets—a software suite offering diverse office functions, including document processing, spreadsheet creation, and presentation tools. This study analyzes user satisfaction levels and the influence of the variables Content, Accuracy, Format, Ease of Use, and Timeliness on WPS Office application users in Jambi City, using the End User Computing Satisfaction method. Data were gathered through an online questionnaire distributed to students in Jambi City who had used the application; created via Google Forms, it garnered 385 responses. Post-collection, analysis was conducted using Structural Equation Modeling in SmartPLS software version 4. Of the five hypotheses tested, four were accepted. The results reveal that accuracy, format, ease of use, and timeliness positively and significantly influence user satisfaction, while content shows no significant effect.
Implementasi YOLOv8 dan Pengaruh Augmentasi Data dalam Sistem Deteksi Faktor Risiko Sudden Infant Death Syndrom (SIDS) pada Bayi
Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.
Penerapan NLP Menggunakan Algoritma Naive Bayes, C4.5, XGBoost untuk Analisis Sentimen Ulasan Produk Kecantikan di Tokopedia dan Shopee
The growth of e-commerce in Indonesia has led to an increase in product reviews, including for beauty products on Tokopedia and Shopee. These reviews serve as important sources of information to assess consumer satisfaction; however, manually analyzing thousands of reviews daily is impractical. This study applies Natural Language Processing (NLP) with Naive Bayes, C4.5, XGBoost algorithms to classify sentiment in Indonesian-language reviews. The dataset used consists of 76,256 reviews labeled as positive, negative, and neutral. The research stages include text preprocessing, feature representation using BoW and TF-IDF, data balancing through SMOTE, and model performance evaluation based on accuracy, precision, and recall. Differences in results among the algorithms were analyzed using ANOVA. The results show that Naive Bayes achieved the highest accuracy at 67.71%, followed by XGBoost at 65.91%, and C4.5 at 58.39%, with Naive Bayes performing best in identifying positive and negative sentiments, while XGBoost and C4.5 handled more complex data patterns effectively. These findings provide guidance for sentiment analysis in Indonesian and support businesses in obtaining automated insights from customer reviews to improve product quality and services.
Perbandingan Algoritma Naïve Bayes Classifier (NBC) dengan Random Forest Untuk Klasifikasi Penyakit Ginjal Kronis (PGK)
Chronic Kidney Disease (CKD) is a heterogeneous disorder that gradually affects the structure and function of the kidneys, is difficult to recover, and causes the body to be unable to maintain metabolism and fail to maintain fluid and electrolyte balance, leading to increased urea levels. Chronic kidney disease data was obtained from Kaggle, in this study a comparison was made between two classification algorithms, namely Naïve Bayes Classifier (NBC) and Random Forest because it is not yet known what algorithm is best in classifying chronic kidney disease (CKD). Both algorithms are evaluated based on performance metrics such as accuracy, precision, recall, and confusion matrix. The results of the evaluation showed that in a dataset of 400 samples, the performance of the Naïve Bayes Classifier (NBC) algorithm obtained an accuracy of 94%, while Random Forest had an accuracy of 93%. Then in the small dataset (158 data), Random Forest got a better accuracy score with 87% compared to the Naïve Bayes Classifier (NBC) of 78%. Based on the results of the evaluation, Random Forest has a more stable performance on small datasets, while Naïve Bayes Classifier (NBC) provides higher performance on larger datasets in the context of chronic kidney disease classification.
Evaluasi Perbandingan Laravel Blade dan Livewire pada Sistem Presensi Akademik
The increasing demand for interactive web applications has encouraged the adoption of server-driven approaches such as Livewire as an alternative to building Single Page Applications (SPAs) without complex client-side JavaScript. However, the performance implications of this approach compared to conventional methods remain insufficiently explored. This study presents an empirical comparison between Laravel Blade with AJAX and Livewire in an academic attendance system scenario. Performance evaluation was conducted using k6 on the same web server, complemented by manual browser-based testing to observe actual communication patterns. The results indicate that Livewire exhibits approximately 2.7× higher average response time and up to 6× greater bandwidth consumption than Laravel Blade, primarily due to its snapshot mechanism and state synchronization process. Conversely, Livewire demonstrates better stability, reflected by lower maximum response times and a 0% error rate. These findings highlight a clear trade-off between resource efficiency and development convenience, where Livewire favors stability and developer productivity, while Laravel Blade provides superior efficiency in terms of latency and bandwidth usage.
Optimalisasi dan Perancangan Sistem Informasi Layanan Pengaduan Masyarakat di Kabupaten Tanjung Jabung Barat
Public complaint services are an essential part of public service delivery in supporting the government’s rapid response to various social issues and emergency situations. In West Tanjung Jabung Regency, public complaint services are provided through the HALO USTAD 112 Call Center managed by the Department of Communication and Informatics. However, the existing service still faces several limitations, including the lack of optimal integration in complaint data management, inadequate documentation of reports based on regional classifications, and limited capabilities in storing and retrieving complaint data. This study aims to optimize the HALO USTAD 112 Call Center service through the design of a mobile-based public complaint information system, so that the processes of receiving, managing, and monitoring reports can be carried out more effectively and in a structured manner. The system development applies the Waterfall method, which consists of requirement analysis, system design, implementation, and testing stages. The designed information system includes key features such as user and admin login, complaint submission, report management and verification, report monitoring, statistical visualization of complaint data, and regional-based report recapitulation. The application is developed using the Flutter framework with the Dart programming language, while Supabase is utilized as the backend integrated with a PostgreSQL database. The results of this study are in the form of a system design and prototype that are expected to improve the quality of public complaint services and support more accurate, integrated, and efficient data management.
Deteksi Serangan pada Internet of Vehicles dengan Algoritma XGBoost dan Feature Selection Information Gain
The rapid development of the Internet of Vehicles (IoV) has introduced new security challenges, particularly in protecting Controller Area Network (CAN Bus) communications from cyberattacks such as Denial of Service (DoS) and spoofing attacks. This study proposes the implementation of the Extreme Gradient Boosting (XGBoost) algorithm combined with Information Gain feature selection to improve intrusion detection performance in IoV environments. The CICIoV2024 dataset, which represents both benign and malicious traffic, is used as the primary data source. The research process includes data integration, preprocessing, feature selection, data splitting, and model training using a 5-fold cross-validation approach. Experimental results demonstrate that the proposed model achieves outstanding performance, with accuracy, precision, recall, and F1-score exceeding 99.99%, and an Area Under Curve (AUC) value approaching 1.00. Furthermore, Information Gain successfully identifies the most influential CAN payload features, enhancing model efficiency without sacrificing accuracy. These findings confirm that the combination of Information Gain and XGBoost is highly effective for developing a fast, accurate, and efficient intrusion detection system in IoV networks.
Pengembangan Sistem Informasi Reservasi Event Menggunakan Metode Prototype
The development of information technology has encouraged restaurants and cafés to function not only as dining places, but also as venues for hosting various events. However, the event reservation process at Rumah Makan Ny. Hartini and Café Rain is still carried out manually through logbooks, telephone calls, and WhatsApp, resulting in problems such as unorganized data, delayed confirmations, and miscommunication with customers. In addition, the manual system limits access to information regarding venue availability, reservation schedules, and additional facilities required by customers. This study aims to develop a web-based event reservation information system using the prototyping method. The system design was carried out using Unified Modeling Language (UML), including use case diagrams, activity diagrams, and class diagrams to model user interactions, process flows, and system structure. The results of the study show that the developed system is able to automate the reservation process, customer data recording, reservation confirmation, schedule management, and additional facilities management. This system improves operational efficiency, data accuracy, and service quality, while also making it easier for customers to make reservations independently and obtain information quickly and accurately.
Klasifikasi Metode Pembayaran Transaksi Menggunakan Algoritma Naïve Bayes dan Support Vector Machine
This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.
Penerapan Metode K-Means Clustering Untuk Menentukan Faktor Resiko Pada Penderita Diabetes Melitus
Diabetes Mellitus is a disease caused by the failure of the pancreas organ in producing the hormone insulin in excess causing increased blood sugar levels and resulting in a lack of insulin. This study discusses the application of the k-means clustering method to determine risk factors for diabetes mellitus. By using the clustering method, data will be grouped into several clusters or groups which in this study compare by applying several data mining tools such as RapidMiner, SPSS, WEKA, and Python. From the results of the comparison carried out resulted in 5 calculations, namely the manual calculation of cluster 1 with a ratio value of 73% being the first priority, calculations using RapidMiner resulting in cluster 3 with a ratio value of 58% being the first priority, calculations using SPSS cluster 2 with a ratio value of 34% being the first priority, and calculations using Python produce cluster 1 with a ratio value of 55% being the first priority.
Perancangan UI/UX Website Vinix Showcase bagi Peserta Program PT Seven Vinix Aurum Menggunakan Metode Design Thinking
This study aims to design the User Interface (UI) and User Experience (UX) on the VINIX Showcase Website as a personal branding platform and digital Skill Passport for participants of the VINIX Seven Aurum Program using the Design Thinking method. The background of this research is the absence of an integrated digital platform that can systematically and easily document and display participants' skills, projects, certificates, and professional identity. The design process is carried out through five stages of Design Thinking, namely Empathize, Define, Ideate, Prototype, and Test, starting with exploring user needs, formulating problems, developing solution ideas, creating Prototypes, and Usability Testing. The results of the study consist of the UI/UX design of the VINIX Showcase Website, which includes registration and Login features, user Dashboard, Skill Passport, project upload, public Showcase, and automatic CV generation feature. Testing using the Usability Testing method showed that the resulting design has a good level of ease of use and comfort and is acceptable to users. This research is expected to be an effective digital solution in supporting personal branding, skills documentation, and improving the professionalism of VINIX Seven Aurum Program participants.
Pengaruh Strategi Pemasaran Digital dan Daya Tarik Konsumen Terhadap Keputusan Mahasiswa dalam Membeli Produk Pakaian di Universitas Dinamika Bangsa Jambi
This study aims to investigate the impact of social media advertising on clothing choices at Universitas Dinamika Bangsa Jambi students. In today's world, where many people, especially young people who frequently shop online, often struggle to accurately determine the quality of items. A quantitative approach was employed, with a survey as the primary method of data collection. A questionnaire was distributed online via Google Forms and successfully elicited responses from 102 active students who are also social media users. The sampling technique used was purposive sampling, with participants selected based on criteria that matched the focus of the study. The data were then processed using SmartPLS 4 software with the Partial Least Squares Structural Equation Modeling (PLS-SEM) method to test the relationship between variables. The main findings indicate that social media promotions have a strong positive influence on students' clothing purchasing decisions. This underscores the crucial role of targeted advertising strategies in the digital world in shaping consumer preferences. This research is expected to serve as a guide for clothing entrepreneurs in developing online marketing plans that better suit the tastes and needs of students as their target market.
Analisis Kepuasan Pengguna Aplikasi VSCO di Kota Jambi dengan Menggunakan Metode EUCS
The use of the VSCO application continues to face technical issues, including errors during editing, limited feature access, and login problems that affect user satisfaction. This study analyzes user satisfaction with the VSCO application using the End User Computing Satisfaction (EUCS) method. The study involved 385 VSCO users as respondents, with data collected through questionnaires and analyzed using SmartPLS 3.0. In this research, Accuracy variable does not affect user satisfaction, whereas the Content, Format, Ease of Use, and Timeliness variables have a significant effect on user satisfaction. The study shows that content quality, interface design, ease of use, and system timeliness are the main factors influencing user satisfaction with the VSCO application.
Sentimen Analisis Review Aplikasi Cek Bansos Pada Google Play Store Menggunakan Metode Naïve Bayes
The Ministry of Social Affairs has made a new breakthrough in facilitating the public in checking social assistance recipients, namely the social assistance check application. User reviews can be used to find out whether the application provides benefits to the community or not. However, these reviews need to be processed using sentiment analysis. Then to do sentiment analysis requires machine learning. One method that includes machine learning is Naïve Bayes. The purpose of this research is to implement the Naïve Bayes method in conducting sentiment analysis and find out whether the social assistance check application is beneficial to society based on the results of sentiment analysis. In this study, two categories of sentiment are used, namely positive and negative. The author collects by crawling using the Google Play Scrapper library. The results of crawling data obtained as many as 4000 data. The results showed that the actual data that had been labeled using Textblob resulted in 987 negative label reviews and 628 positive label reviews. Meanwhile, the Naïve Bayes method is able to analyze the review sentiment of the social assistance check application with the results of 1181 negative sentiments and 434 positive sentiments. The Naïve Bayes model has a good accuracy rate of 0.77 or 77% in analyzing sentiment for social assistance check application reviews.
Penerapan Machine Learning untuk Klasifikasi Tingkat Produktivitas Pabrik Karet GAPKINDO Jambi
Jambi Province is one of the largest natural rubber producing regions in Indonesia; however, rubber factories under GAPKINDO Jambi still face productivity issues, particularly the gap between production capacity and actual output, and productivity assessment that is still conducted manually by GAPKINDO Jambi. This study employs Decision Tree, Random Forest, KNN, and SVM algorithms within a structured pipeline involving preprocessing, feature selection, standardization, data balancing using SMOTE, and hyperparameter tuning. The proposed solution applies productivity level classification both individually and through paired combinations (ensemble voting). The results show that the Decision Tree + Random Forest model achieves the best performance with an accuracy of 0.84 and an F1-score of 0.83, confirming the effectiveness of ensemble methods in supporting productivity improvement decisions.
Analisis Performa Model Random Forest dan Support Vector Regression untuk Prediksi Suhu Maksimum Harian di Kota Jambi
The dynamic changes in weather patterns in Jambi City require an accurate temperature prediction system, thus this study aims to compare the performance of Random Forest and Support Vector Regression (SVR) algorithms in predicting daily maximum temperatures using weather data from 2020–2024 obtained from Open Meteo with the application of Feature Engineering including lag and rolling window features. The test results indicate that the SVR model with a Radial Basis Function (RBF) kernel optimized using Grid Search (C=10, epsilon=0.2, gamma=0.01) significantly outperforms Random Forest based on a statistical Paired T-test (p-value < 0.05), yielding an R-squared (R²) value of 87.46%, Mean Absolute Error (MAE) of 0.3818 °C, and Root Mean Squared Error (RMSE) of 0.4964 °C compared to Random Forest's R² of 84.05%, where the previous day's temperature (lag) and three-day rolling average were identified as the most dominant predictors, leading to the recommendation of SVR as the more effective method for temperature prediction in the study area.
Deteksi Serangan Mirai Pada IoT Menggunakan Recurrent Neural Network (RNN) dengan Optimasi Hyperparameter Berbasis Bayesian Optimization
The massive adoption of Internet of Things (IoT) devices is expanding the cyberattacks surface, particularly by the Mirai botnet, which exploits the dynamic characteristics of data traffic. This research proposes a Mirai detection approach based on a Recurrent Neural Network (RNN) optimized using Bayesian Optimization to improve prediction accuracy on sequential data. Unlike previous studies, this research utilizes the latest CIC IoT-DIAD 2024 dataset and applies probabilistic optimization to the hyperparameter space, including RNN units, dropout, and learning rate. The experiment was conducted on 201,021 valid data points, with dimensionality reduction using PCA as the optimal point to represent essential features without redundancy. The results show a significant increase in accuracy from 97.95% to 99.69%, accompanied by an 84% decrease in False Negatives, an 86% decrease in False Positives, and an AUC value of 0.9999. These findings confirm that integrating RNN and Bayesian Optimization not only improves numerical performance but also strengthens the reliability of the intrusion detection system for modern IoT ecosystems with controlled computational loads.
Arsitektur Enterprise Sistem Pemerintahan Berbasis Elektronik (SPBE) Domain Arsitektur Proses Bisnis pada Desa Sido Rukun
The implementation of the Electronic-Based Government System (SPBE) is essential for achieving efficient, transparent, and accountable village governance. Sido Rukun Village in Merangin Regency, Jambi Province, has begun using several government applications but lacks a structured enterprise architecture aligned with the national SPBE framework. This study aims to develop an enterprise architecture for SPBE in the business process domain at Sido Rukun Village. The research employs the TOGAF ADM (The Open Group Architecture Framework – Architecture Development Method) approach, involving stages such as identifying current business processes, designing a target architecture, and conducting a gap analysis between the as-is and to-be states. The findings include a business process architecture blueprint compliant with Presidential Regulation No. 95 of 2018 and Presidential Regulation No. 132 of 2022 on the National SPBE Architecture. This blueprint encompasses BPMN-based business process models and supporting artifacts that serve as a foundation for integrated information systems at the village level. The study’s implications are significant: it provides Sido Rukun Village with a practical and standardized technical blueprint for implementing a sustainable electronic-based government system, thereby supporting its transformation toward a Smart Village capable of adapting to evolving information and communication technology trends.
Perancangan Sistem Informasi Jasa Jahit dan Penjualan Pada Toko Jahit SA’aminah Berbasis Web
Toko Jahit SA’aminah is a business engaged in tailoring services and the sale of sewing supplies that still manages data manually using record books. This condition causes several problems, such as slow data recording, the risk of data loss or damage, difficulties in monitoring the status of tailoring work and inventory, as well as obstacles in preparing tailoring service and sales reports. This study aims to design and develop a web-based tailoring service and sales information system to optimize the effectiveness and efficiency of operational performance. The system development method used is the waterfall method, which includes the stages of requirements analysis, system design using UML (Use Case Diagram, Activity Diagram, and Class Diagram), implementation using the Laravel framework with the PHP programming language and MySQL database, as well as system testing using the Black Box Testing method. The results show that the developed system is able to facilitate the management of tailoring service and sales data, monitor the status of tailoring work, check the availability of sewing supplies, and accelerate the preparation of tailoring service and sales reports to be submitted to the owner of Toko Jahit SA’aminah.
Pengaruh Disiplin Kerja, Kompensasi dan Lingkungan Terhadap Kinerja Karyawan PT Dunia Aneka Usaha
Employee performance is a strategic aspect that plays an important role in achieving company goals, especially in facing increasingly fierce business competition. Employee performance is not formed directly, but is influenced by various internal organizational factors, such as work discipline, compensation systems, and work environment conditions. This study aims to analyze the influence of work discipline, compensation, and work environment on employee performance at PT Dunia Aneka Usaha. This study uses a descriptive quantitative approach. The sampling technique used is a saturated sample, so that the entire population of 70 employees was used as research respondents. Data were collected through questionnaires compiled based on indicators of each variable and measured using a Likert scale. Furthermore, the data were analyzed using validity and reliability tests to ensure the quality of the research instrument, as well as statistical analysis in the form of t-tests, F-tests, and multiple linear regression analysis to test the proposed hypotheses. The results of the study indicate that work discipline, compensation, and work environment have a positive and significant effect on employee performance, both partially and simultaneously. These findings indicate that improving work discipline, providing appropriate compensation, and creating a conducive work environment can encourage improved employee performance. Therefore, companies are advised to manage these three factors sustainably so that employee performance improves and organizational goals can be achieved optimally.
Pengaruh Social Media Marketing, Kualitas Produk dan Lokasi Terhadap Keputusan Pembelian di Gerai Adila
With the rapid growth of Jambi City's tourism sector, the demand for souvenir products as a representation of local culture has increased. However, this potential has resulted in fierce competition among businesses. To generate new customers, businesses require effective digital and physical marketing strategies. The purpose of this study was to examine how social media marketing, product quality, and location at Gerai Adila simultaneously and partially influence consumer purchasing decisions. A survey method was used to implement a quantitative approach; data from 387 respondents, collected through Google Forms, were selected using the Slovin formula from a population of 11,723 customers in 2024. In data analysis, instruments, classical assumptions, multiple linear regression, and hypothesis testing were conducted using SPSS 25. The results showed that social media marketing, product quality, and Gerai Adila's location partially had a positive and significant impact on consumer decisions to purchase products at Gerai Adila; social media marketing played a role in increasing consumer brand awareness and purchasing interest, while product quality influenced consumer satisfaction and trust. In addition, it was proven that all three variables had a positive and significant impact on purchasing decisions. The results show that to increase the competitiveness of souvenir shops in Jambi City, digital marketing strategies, consistent product quality, and the right location are important factors.
Klasifikasi Tumor Otak pada Citra MRI Menggunakan Transfer Learning EfficientNetB1 dan Visualisasi Grad-CAM
Magnetic resonance imaging (MRI) provides rich anatomical contrast for brain tumor assessment, yet routine interpretation remains time-intensive and demands high precision. This work develops a pipeline for four-class brain MRI image classification (glioma, meningioma, pituitary tumor, and no tumor) by combining automated brain-region cropping, data augmentation, and transfer learning with EfficientNetB1. Experimental results demonstrate exceptional performance, achieving an overall accuracy of 0.99 (99%) on the test set. Specifically, the model reached an F1-score of 1.00 for the no tumor class, 0.99 for pituitary, and 0.98 for both glioma and meningioma classes. Beyond reporting numerical performance, the study utilizes Grad-CAM heatmaps to verify that predictions rely on clinically plausible regions rather than spurious background cues. These results indicate that an efficiency-oriented backbone, paired with systematic preprocessing, can achieve reliable and interpretable performance for brain tumor classification tasks.
Analisis dan Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Penyakit Diabetes Melitus
Diabetes Mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action or both. Hyperglycemia is a medical condition in the form of an increase in glucose levels beyond normal limits which is a characteristic of several diseases, especially Diabetes Mellitus, in addition to various other conditions. Diabetes Mellitus is currently a global health threat. Classification is one of the techniques of data mining that can be used to help predict the results of the classification of types of diabetes using the naïve Bayes algorithm. Testing was carried out using 5 evaluation models including rapid miner with 3 options, namely use training set, 5 Fold Cross-Validation, 10 Fold Cross-Validation, and 2 other evaluation models, namely Microsoft Excel and Python. Testing data regarding Diabetes Mellitus has high accuracy in the excel evaluation model, which is 89.00% compared to other evaluation models. Meanwhile, the lowest accuracy is the Python evaluation model which obtains an accuracy of 86.36%. The Naïve Bayes algorithm can be said to be one of the most effective algorithms, both in terms of calculations and the final results, where the test can be used as a basis for diabetes mellitus considering the accuracy results are above 85%.
Pengaruh Harga dan Ulasan Produk Terhadap Keputusan Pembelian Konsumen pada Marketplace Shopee Studi Kasus Masyarakat Kota Jambi
This research aims to examine how prices and reviews influence consumers 'purchasing decisions on the Shopee platform in Jambi City. The rapid development of e-commerce has encouraged consumers to be more careful and discerning when selecting products, making price and product reviews important considerations in buying decisions. This study employed a quantitative approach via a survey method for data collection. The questionnaires were distributed online and successfully gathered responses from active Shopee users in Jambi City. Purposive sampling was employed to make sure that respondents met research criteria. The information gathered underwent analysis using IBM SPSS Statistics 27, which involved conducting assessments of validity, reliability, and classical assumptions, and multiple regression tests to see the impact of each variable on buying decisions. The study revealed that product price and reviews hold an important and relevant impact on consumer buying decisions. This research indicates that the more competitive the price and the better the quality, the bigger the possibility of customers buying products on Shopee.
Analisis Dualitas Kesejahteraan: Pengaruh Korupsi dan Stabilitas Ekonomi terhadap Human Development Index dan Indeks Kebahagiaan di Negara ASEAN (2015-2023)
This study focuses on analyzing the welfare index in the ASEAN region (covering six major countries) by comparing two perspectives: objective welfare (Human Development Index/HDI) and subjective welfare (World Happiness Index). Using a balanced panel dataset from 2015–2023, the research applies different econometric approaches for each model, namely the Random Effect Model (REM) for HDI analysis and the Common Effect Model (CEM) for happiness analysis. Empirical findings indicate a striking welfare paradox across the six sample countries. In the objective dimension (HDI), economic stability (GDP) and governance free from corruption (CPI) are proven to be the main positive and significant drivers, while government expenditure (GovExp) shows no meaningful impact, suggesting budget inefficiency. Conversely, in the subjective welfare model, the Easterlin Paradox emerges, as GDP and the corruption index have no significant effect on the happiness index. The happiness levels in these six countries tend to be more influenced by government expenditure. This study concludes that strong economic fundamentals and clean governance free from corruption are essential to building a high quality of human life, whereas citizens’ life satisfaction is more determined by the direct presence of the state through public spending.
Perbandingan Efektivitas QRIS dengan Metode Pembayaran Non-Tunai Lain dalam Mendorong Ekonomi Digital
This study aims to compare the effectiveness of QRIS with other cashless payment methods in driving the development of the digital economy in Indonesia. The background to this study stems from the increasing use of digital transactions and the strategic role of QRIS as a national standard for QR-based payments. Google Forms was used to disseminate the survey online and successfully collected 100 respondents who were users of cashless digital payment services. A purposive sampling technique was used to ensure that respondents had experience using QRIS and other digital payment methods. Data were examined using the SmartPLS 4 program using a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach to test the relationships between variables. The results indicate that perceived ease of use, transaction security, and influence on digital behavior have a positive or significant influence on QRIS effectiveness. However, availability and accessibility variables did not have a significant effect. This finding indicates that QRIS effectiveness is more influenced by user experience and perceptions, rather than availability or ease of access. This research is expected to contribute to the development of strategies to increase digital payment adoption in Indonesia.
Persepsi Masyarakat Terhadap Keseimbangan Kerja dan Hidup (Work-Life Balance) Perempuan Karier: Studi di Lingkungan Masyarakat Kota Jambi
This research aims to analyze the public's perception of career women's efforts in achieving work-life balance, as well as its impact on the dual role experience. The research focus includes the view of society, obstacles experienced by women, and the social implications of the dual role of women. The results of the study based on the questionnaire showed that 74,94% of the people had a positive perception of career women and judged that the balance of work and life can be achieved through time management and family support, while 25,06% had a neutral to negative perception that considered that career women were not optimal in carrying out domestic roles, thus causing social pressure and role conflicts. This perception contributes to stress levels, emotional fatigue, and difficulties in achieving work-life balance. This finding also reveals that education factors, working experience with women, family roles, and social environment affect the formation of perceptions. This research is expected to contribute in increasing public understanding and encouraging policies that support women's careers both in social and work.
Analisis Kualitatif Hubungan antara Persepsi Kualitas, Kepercayaan, dan Loyalitas: Perspektif Pelanggan terhadap Merek
In the contemporary digital marketplace, consumers increasingly face diverse product choices and brand communications. Understanding the mechanisms through which product quality and brand perception influence customer loyalty remains critical for competitive advantage. The mediating role of customer trust in this relationship has received limited empirical attention within Indonesian market contexts. This research analyzes the direct and indirect effects of product quality and brand perception on customer loyalty, with customer trust as a mediating variable, using Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology on 103 respondents. A quantitative cross-sectional survey design was employed, collecting data via Likert-scale questionnaires (1-5) with 15 measurement items across four latent constructs: Product Quality (5 items), Brand Perception (4 items), Customer Trust (3 items), and Customer Loyalty (3 items). Data analysis utilized PLS-SEM via SmartPLS 3.0, including assessment of measurement model validity (outer model), structural relationships (inner model), and mediation effects through bootstrapping (5000 iterations). The outer model demonstrated adequate validity with 12 of 15 indicators loading above 0.7, and all constructs meeting composite reliability (CR > 0.7) and average variance extracted (AVE > 0.5) criteria. The inner model revealed that product quality significantly influenced customer trust (β = 0.624, p < 0.001), while brand perception showed no significant direct effect (β = 0.045, p = 0.767). Customer trust strongly predicted loyalty (β = 0.650, p < 0.001). Product quality demonstrated a significant indirect effect on loyalty through trust (β = 0.405, p < 0.001), indicating full mediation. The model explained 43.5% of trust variance and 42.2% of loyalty variance. Product quality emerged as the dominant antecedent of customer trust and loyalty, while brand perception did not significantly contribute. Trust served as the critical mechanism translating quality into loyalty. These findings suggest that companies should prioritize quality assurance and consistent delivery over brand marketing campaigns for sustainable loyalty development. The research contributes to mediation theory in consumer behavior and provides actionable strategic guidance for practitioners in emerging markets.
Analisis Tata Kelola Teknologi Informasi pada Universitas Adiwangsa Jambi Menggunakan Framework COBIT 2019 Domain EDM (Evaluate, Direct, Monitor)
Information technology (IT) governance plays a strategic role in supporting the achievement of organizational goals, especially in higher education institutions. Adiwangsa University Jambi, as a private university, is required to manage IT effectively, efficiently, and in line with the institution's vision. This study aims to analyze the level of IT governance capabilities at Adiwangsa Jambi University using the COBIT 2019 framework with a focus on the EDM (Evaluate, Direct, Monitor) domain. The research method used is a descriptive quantitative method through data collection using questionnaires, interviews, and documentation studies. The results show that the level of IT governance capability in the EDM domain is at level 2 (Managed Process), indicating that the IT governance process is running but still needs improvement in several aspects, especially in monitoring and controlling IT performance. This study is expected to provide recommendations for Adiwangsa University Jambi in improving IT governance in a sustainable manner.
Klasifikasi Sentimen Ulasan Produk Olahraga di Tokopedia Menggunakan Metode Machine Learning dengan Pendekatan TF-IDF
The rapid growth of e-commerce platforms in Indonesia, particularly Tokopedia, has resulted in a large volume of consumer reviews containing valuable information regarding customer perceptions and satisfaction. However, manual analysis of such reviews is inefficient and prone to subjectivity, necessitating an automated approach based on machine learning. This study aims to classify the sentiment of sports product reviews on Tokopedia into positive, negative, and neutral categories by applying Logistic Regression, Support Vector Machine (SVM), and Random Forest using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. The data were collected through web scraping of Indonesian-language sports product reviews and processed through several preprocessing stages, including data cleaning, case folding, tokenization, stopword removal, and stemming. Feature representation was performed using TF-IDF to transform textual data into numerical vectors, after which the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of TF-IDF significantly improves the performance of all models, with SVM consistently achieving the most optimal performance compared to Logistic Regression and Random Forest. These findings demonstrate that classical machine learning algorithms combined with TF-IDF remain highly effective for sentiment analysis of Indonesian-language text. The implications of this study are expected to assist sellers in understanding customer opinions, support consumers in making informed purchasing decisions, and serve as a foundation for the development of sentiment analysis and recommendation systems on e-commerce platforms.
Pengaruh Promosi, Kemudahan Berbelanja, dan Kredibilitas terhadap Intensitas Pembelian di TikTok Shop pada Konsumen di Universitas Dinamika Bangsa
This study aims to analyze the influence of promotion, shopping convenience, advertising, recommendation, comment, and credibility factors on purchase intensity in the TikTok Shop platform among Indonesian households. The rapid development of social commerce services requires a deep understanding of factors driving online purchasing decisions, especially among families as primary users. A quantitative approach was employed, utilizing a Likert-scale questionnaire distributed online. The sample consisted of 150 active TikTok Shop users from various household backgrounds. Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS was used to analyze the direct effects of each factor on purchase intensity. The results indicate that promotion, shopping convenience, and credibility significantly and positively influence purchase intensity, while advertising, recommendation, and comment have relatively weaker yet still relevant effects. These findings underscore the importance of effective promotional strategies, ease-of-shopping enhancements, and building platform credibility to boost purchase intensity within the household consumer segment. The practical implications can guide e-commerce practitioners and digital marketers in formulating adaptive marketing strategies in the era of social commerce.
Analisis Faktor-Faktor yang Mempengaruhi Tingkat Kemiskinan Antar Provinsi di Indonesia Tahun 2018–2024
The purpose of this study is to investigate the factors that lead to poverty in different Indonesian regions between 2018 up to 2024. The Gross Regional Domestic Product per capita, the Human Development Index, and the Open Unemployment Rate are the independent factors used in this study, whereas poverty levels are the dependent variable. The Central Statistics Agency provided secondary data that was used in a quantitative manner. Using EViews 12 software, panel regression techniques were used to process the data. The study's conclusions show that, at a significance level of less than 0.05, economic and human development factors simultaneously significantly affect poverty rates across Indonesian regions. The coefficient of determination indicates that the variables in the model can account for the majority of the variations in poverty levels. These findings demonstrate how important a region's economic status and level of human development are to efforts to reduce poverty. It is anticipated that this research will help the government develop more effective and long-lasting methods for reducing poverty.
Pengaruh Harga dan Fasilitas Terhadap Loyalitas Pelanggan Melalui Kepuasan Sebagai Variabel Intervening Pada Member Velcro Gym Kota Jambi
The fitness industry has shown rapid growth as public awareness of the importance of healthy lifestyles increases in today's world. Gyms no longer function solely as a place to exercise, but also offer value and a sustainable experience for their members. The ability to manage a gym and retain members in this increasingly competitive era encourages gym owners to be more sensitive to factors that could potentially affect member satisfaction and loyalty. To improve the quality of the gym, it is necessary to evaluate the price and quality offered in the form of well-maintained and complete facilities. The suitability of the price to the benefits felt by members will make them more loyal to training at the gym. Likewise, comfortable facilities can increase member loyalty, which in turn drives member satisfaction, especially at Velcro Gym.
Optimasi Prediksi Hipertensi Menggunakan Logistic Regression Berbasis Borderline SMOTE dan Penjelasan Model dengan SHAP
Hypertension is a major global health risk that requires accurate early detection, yet conventional methods struggle with complex and imbalanced health datasets. This study aims to optimize hypertension prediction using a Logistic Regression model integrated with Borderline-SMOTE to enhance recall and provide model transparency through SHAP (Shapley Additive Explanations). The method utilizes the BRFSS dataset, applying Borderline-SMOTE to address class imbalance at the decision boundary and XAI techniques for global and local interpretation. The findings show that the model achieved an accuracy of 0.719, an AUC of 0.800, and a significantly improved recall of 0.756. SHAP analysis identified age, high cholesterol, and BMI as the most influential risk factors, while waterfall plots successfully clarified individual risk extremes, ranging from 1.72% to 99.43% probability. These results imply that the proposed approach provides a sensitive and transparent screening tool for public health practitioners, effectively balancing statistical efficiency with clinical accountability.
Analisis Kesesuaian Teknologi ChatGPT terhadap Aktivitas Perkuliahan Mahasiswa Menggunakan Model Task–Technology Fit (TTF)
The rapid development of artificial intelligence, particularly ChatGPT, has created new opportunities to support students’ academic activities in higher education. However, its utilization needs to be evaluated in terms of the alignment between academic task characteristics and technological capabilities to ensure optimal outcomes. This study aims to examine the feasibility of using ChatGPT in students’ academic activities by applying the Task–Technology Fit (TTF) model. This research employed a quantitative approach using Structural Equation Modeling based on Partial Least Squares (SEM-PLS). Data were collected through questionnaires distributed to university students and analyzed using SmartPLS 4 software. The variables examined included Task Characteristics, Technology Characteristics, Task–Technology Fit, Performance Impact, and Utilization. The results indicate that Task Characteristics and Technology Characteristics have a positive and significant effect on Task–Technology Fit. Furthermore, Task–Technology Fit significantly influences Performance Impact and Utilization. Performance Impact also shows a positive and significant effect on the utilization of ChatGPT by students. These findings suggest that the alignment between academic task requirements and the capabilities of ChatGPT plays a crucial role in improving students’ performance and encouraging sustained technology use. The implications of this study highlight the importance of selective and purposeful use of ChatGPT in higher education and provide a reference for higher education institutions in formulating policies related to the ethical and effective integration of artificial intelligence technologies as learning support tools.
Model Deteksi Mahasiswa Dropout Menggunakan Artificial Neural Network
Student dropout remains a significant challenge for higher education institutions as it impacts academic quality, educational management efficiency, and students' success in completing their studies. Therefore, an approach that can identify students at risk of dropping out is necessary so that timely academic interventions can be made. This study aims to develop a dropout detection model using an Artificial Neural Network (ANN). The data used come from a publicly available higher education dataset, ensuring research reproducibility. Data preprocessing steps were carried out to improve data quality before modeling, and the Synthetic Minority Over-Sampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) was applied to address class imbalance issues. The ANN model's performance was evaluated using accuracy, precision, recall, F1-score, and area under the ROC curve (ROC-AUC). The test results show that the ANN model can provide excellent predictive performance in detecting at-risk students. The application of SMOTE-ENN also proved to enhance the model’s sensitivity toward the minority class, as indicated by improvements in recall and F1-score. These findings indicate that the developed ANN model has the potential to be used as a student dropout detection system to support data-driven decision-making and strategy development within higher education institutions.
Analisis Tata Kelola Teknologi Informasi Menggunakan Framework COBIT 5.0 Pada SMP Negeri 5 Merlung
SMP Negeri 5 Merlung is a public junior high school in Merlung Subdistrict that has utilized the DAPODIK system for online data processing management, enabling efficient sending and receiving of information to the government. This research analyzes IT governance on the DAPODIK system using the COBIT 5 framework, specifically the MEA01 domain (Monitor, Evaluate and Assess Performance and Conformance), which focuses on monitoring and evaluating performance and conformance. The research background is based on the need to maximize the utilization of the system at the school level. The main objectives are to determine the current and expected capability levels, as well as to provide improvement recommendations to achieve higher process maturity. The research method applies Assessment Process Activities, covering observation, interviews, identification of findings, gap analysis, and recommendations. The results show that the current capability level is at level 3 (established process), while the expected capability level is directed toward level 4 (predictable process). The implications of these findings provide practical recommendations such as routine monitoring enhancements, staff training, and integration of automation tools to bridge the capability gap, thereby improving the effectiveness of IT governance at SMP Negeri 5 Merlung sustainably.
Deteksi Serangan ARP Spoofing MITM pada Jaringan IoT menggunakan Metode Random Forest dan Robust PCA
ARP spoofing attacks are a serious threat to network security, particularly in vulnerable Internet of Things (IoT) environments. This final project aims to detect ARP spoofing attacks on IoT net-works using a combination of Random Forest (RF) and Robust PCA methods. RF is chosen for its classification capabilities and handling of non-linear data, while Robust PCA is used for di-mensionality reduction and handling outliers in the data. The dataset used is "MITMArpSpoof-ing.pcap.csv," which contains network traffic data. The data is processed by performing prepro-cessing, feature scaling, and converting labels to binary (0 for benign, 1 for ARP spoofing). Subsequently, Robust PCA is applied to reduce data dimensions, and then the data is trained using the RF model. The test results show that the RF model with Robust PCA achieves an accu-racy of 96.02% in detecting ARP spoofing attacks. This method has proven effective in identify-ing and classifying ARP spoofing attacks on IoT networks.
Analisis Kelayakan Pemberian Kredit dengan Algoritma Naïve Bayes untuk Antisipasi Risiko Kredit Bermasalah Pada BPR Ukabima Lestari Cabang Jambi
This study analyzes creditworthiness assessment and predicts non-performing loan (NPL) risk using the Naïve Bayes algorithm at BPR Ukabima Lestari, Jambi Branch. A quantitative data mining approach with probabilistic classification is applied. The dataset includes borrower attributes such as age, occupation, income, loan amount, tenor, collateral, and repayment history. Research stages comprise data preprocessing, model development, and performance evaluation using accuracy, precision, recall, and F1-score implemented in RapidMiner. The results indicate that the Naïve Bayes model achieves 99.58% accuracy, demonstrating strong capability to predict potential problem loans accurately and efficiently, supporting data-driven credit decisions and strengthening credit risk management in microbanking institutions.
Sistem Pendukung Keputusan Pemilihan Siswa Terbaik Untuk Olimpiade Olahraga Siswa Nasional (O2SN) Menggunakan Metode Topsis di SMA Negeri 2 Muaro Jambi
Student selection for the National Student Sports Olympiad (O2SN), particularly in the karate category, is still largely conducted manually, which may lead to subjectivity and inconsistency in the assessment process. This study aims to design and develop a web-based Decision Support System (DSS) to assist schools in selecting the best students for the O2SN karate competition by applying the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method at SMA Negeri 2 Muaro Jambi. The system was developed using the waterfall model, which consists of requirement analysis, system design, implementation, and testing stages. The student evaluation process is based on criteria referring to the official O2SN karate standards, including stance accuracy, techniques, movement transitions, timing and harmony, breathing control, focus (kime), conformity (consistency with ryu-ha/style), strength, speed, and balance. The developed system processes assessment data to generate preference values and student rankings, which are separated by male and female categories. The results indicate that the application of the TOPSIS method is able to support more objective decision-making and improve transparency and efficiency in the O2SN student selection process within the school environment.
Perancangan Sistem Pendukung Keputusan Untuk Seleksi Siswa Non-Akademik Cabang FLS3N di SMA Negeri 2 Muaro Jambi dengan Metode Topsis
The National Student Arts Festival and Competition (FLS3N) is an event aimed at developing students’ talents and achievements in the arts, including solo vocal competitions. The assessment process in this category involves multiple criteria, which may lead to subjectivity in decision-making. This study aims to design and develop a web-based Decision Support System (DSS) for selecting non-academic students in the FLS3N solo vocal category using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The assessment criteria are based on the 2025 FLS3N Technical Guidelines, consisting of voice quality, vocal technique, expression, and performance. The TOPSIS method is applied to generate alternative rankings based on the highest preference value. The system is developed using a web-based software development approach and tested using participant data from both male and female categories. The results indicate that the system can provide objective and consistent ranking recommendations, thereby assisting schools in selecting the best students to represent them in the FLS3N competition.
Perancangan Aplikasi Augmented Reality Sebagai Edukasi Nutrisi dan Perkembangan Janin Bagi Ibu Hamil Berbasis Android Pada Klinik Bidan Rima Pondok Meja
The rapid development of information technology encourages the use of digital media as an educational tool in the health sector, particularly for pregnant women. One of the problems faced by Klinik Bidan Rima Pondok Meja is the limited use of conventional educational media, such as books and posters, which are considered less attractive and difficult to understand. This study aims to design and develop an Android-based Augmented Reality (AR) application as an educational medium for nutrition and fetal development for pregnant women. The application presents three-dimensional (3D) visualizations of fetal development from week to week, along with information on nutritional needs during pregnancy. The system development method used in this research is the Prototype model, while the Augmented Reality technology applies marker-based tracking. The development tools used include Unity, and Blender 3D. The result of this study is an Android-based AR application prototype that provides interactive and easily understandable information about fetal development and maternal nutrition. This application is expected to increase learning interest and understanding of pregnant women in maintaining a healthy pregnancy at Klinik Bidan Rima Pondok Meja.
Analisis dan Perancangan Sistem Informasi Perencanaan BLUD Rumah Sakit Berbasis Web dengan Pendekatan Enterprise Application Integration (EAI)
Managing Regional Public Service Agency (Badan Layanan Umum Daerah/BLUD) hospitals requires planning and budgeting processes that are accountable, measurable, and aligned with service performance. In practice, BLUD planning is still constrained by fragmented applications (hospital information system/SIMRS, finance, human resources, e-office, and procurement), duplicate data entry, approval delays, and limited monitoring of process compliance. This study aims to analyze requirements and design a web-based BLUD planning information system using an Enterprise Application Integration (EAI) approach through middleware to improve cross-system interoperability, data consistency, and the timeliness of executive reporting. The study adopts the Design Science Research (DSR) framework, comprising problem identification, definition of solution objectives, artifact design and development, demonstration, evaluation, and communication/report writing. The proposed system includes a unit-based budget proposal module and item management, a role-based approval workflow (RBAC) with SLA tracking, a budget ceiling (pagu) master to benchmark proposals, audit trails and report exports, and an executive dashboard integrating budget perspectives, service indicators (e.g., bed occupancy rate/BOR and patient visits), and process compliance. It also provides an integration design via middleware (ESB/message broker) supported by a canonical data model (CDM) and traceable logging (trace_id/correlation_id). Evaluation using black-box testing and API contract testing indicates that the main planning workflow operates as intended and the integration interfaces are consistently defined, providing a foundation for staged implementation and further performance evaluation.
Optimasi Software Effort Estimation Menggunakan Random Forest
Software development effort estimation is crucial as it is one of the key factors for successful software development. This research employs Random Forest to estimate software development effort. To achieve better results, the study combines the Random Forest method with Genetic Algorithm. The results show that the China dataset provides more accurate estimation compared to the Desharnais dataset, because the China dataset uses relevant feature selection for estimation.
Penerapan Algoritma K-Means Untuk Klasterisasi Balita Rentan Stunting dan Wasting Berdasarkan Indikator Antropometri
Stunting and wasting are nutritional problems in toddlers that remain a double burden of malnutrition in Indonesia and have an impact on the quality of health and future human resource development. Monitoring the nutritional status of toddlers is generally carried out using anthropometric indicators, but the use of this data is still limited to descriptive analysis. This study aims to apply the K-Means algorithm in clustering infants vulnerable to stunting and wasting based on anthropometric indicators, so that groups of infants with different levels of nutritional vulnerability can be identified. The dataset used consists of infant data with variables of gender, age (months), height (cm), and weight (kg). The research stages included data preprocessing, encoding categorical variables, data normalization, determining the optimal number of clusters using the Elbow and Silhouette Score methods, and analyzing the characteristics of each cluster. The evaluation results showed that the optimal number of clusters was four. Each cluster has different anthropometric characteristics and distributions of stunting and wasting status, ranging from groups with relatively normal nutritional conditions, groups with a tendency toward overnutrition, to groups that are vulnerable to acute and chronic malnutrition. These clustering results provide a more comprehensive and segmented mapping of toddlers, which can be used as a basis for formulating more targeted and data-driven nutrition policies and interventions.
Analisis Sentimen Ulasan Aplikasi CapCut Berbasis IndoBERT dengan Validasi Silang
This study conducts sentiment analysis of Indonesian user reviews of the CapCut application using IndoBERT and compares two evaluation schemes: a single 80/20 train–test split and stratified 5-fold cross-validation (k=5). A total of 1,048,575 reviews were collected from the Google Play Store through web scraping and labeled into three sentiment classes based on rating: negative (1–2), neutral (3), and positive (4–5). After preprocessing—cleaning, case folding, banned-word removal, normalization—and duplicate removal, 517,962 reviews were retained. IndoBERT Base P1 was fine-tuned using fixed hyperparameters (batch size 32, learning rate 2e-5, up to 4 epochs, early stopping patience 2), while undersampling was applied to the training set to address class imbalance. Performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC, supported by confusion matrix and ROC-curve visualizations. The single split achieved an accuracy of 0.756, whereas cross-validation produced a mean accuracy of 0.740. Across both schemes, the positive class achieved the best performance (F1-score 0.850; ROC-AUC 0.918–0.919), while the neutral class remained the most challenging (precision 0.198–0.206; F1-score 0.280–0.283). Overall, cross-validation is recommended for reporting because it reduces dependence on a single partition and provides a more representative estimate across multiple splits.
Perancangan Web Consultant Berbasis Website Full Stack “Bizstarter”
Many new business owners are seeking easy and simple business consulting services amid rapid technological advances. Unfortunately, most available platforms only offer consulting, not a place to consult and create websites. Biz Starter was created to meet this need, by becoming a platform that not only offers consulting services but also helps users build websites for their own businesses. The development process uses a user-oriented approach, starting from identifying needs, designing the appearance, and then conducting testing. The results show that it is quite helpful for users, especially beginners, because the appearance is simple, the flow is clear, and the business process is much more practical. Overall, this platform has succeeded in making the user experience easier, and it can serve as a digital companion for people who are preparing for business consulting.
Perancangan Sistem Portofolio Berbasis Website Pada PT. XYZ
Digital portfolios are a crucial tool for professionally showcasing abilities and learning objectives. Nevertheless, the projects and certificates of participants in the Independent Study program at VINIX7 are still kept apart and are not controlled by an integrated system. This requirement is the basis for the research's design and development of a website-based digital portfolio system that will serve as participants' key platform. Requirements analysis, system design, implementation, and testing are all steps in the Waterfall approach of system development. The Laravel framework was used to create the VinixPort website, which is backed by a MySQL database. The system has tools for managing portfolio material, talent evaluation, user registration and login, and data presentation via analytics. The study's findings show that the VinixPort website was created successfully and that all of the system's primary features work as intended. This service helps users create organized digital portfolios that are readily available and prepared for both professional and academic use.
Perancangan UI/UX Website Self-Assessment Berbasis Metode Design Thinking
The skill mismatch between university graduates and technology industry requirements remains a significant challenge in Indonesia. PT Vinix Seven Aurum requires an assessment tool to objectively identify the initial competencies of MBKM and internship program participants. This research aims to design a web-based self-assessment platform that helps students measure their skill gaps against industry standards through radar chart visualization and personalized learning recommendations. The UI/UX design applies the Design Thinking method with empathize, define, ideate, prototype, and test phases, utilizing Figma for wireframe and high-fidelity prototype development. Data collection was conducted through observation, interviews, literature studies, and usability testing with 10 respondents. The results demonstrate good usability with a 100% completion rate across all features. The VINIX Skill Radar platform provides five assessment categories, a 1-10 rating scale system, radar chart visualization, gap analysis, and learning recommendations. This system enhances students' self-awareness of their competencies and supports effective mapping of training program participants' capabilities.
Pengembangan Sistem Repositori Karya Ilmiah Berbasis Web di Fakultas Hukum Universitas Jambi
The management of scientific works at the Faculty of Law, Universitas Jambi, is still carried out manually through physical archives and simple digital storage without an integrated information system. This condition causes limited access, slow information retrieval, and a high risk of document loss and damage. This study aims to develop a web-based scientific repository system to improve the efficiency, accessibility, and security of managing academic works. The system was developed using the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and evaluation. Data collection was conducted through interviews, observations, and questionnaires involving librarians, lecturers, and students. The system was implemented using the Laravel framework with a MySQL relational database to support document storage, metadata management, access control, and search functionality. The results show that the developed repository system is able to facilitate the submission, verification, publication, and retrieval of scientific works effectively. User acceptance testing indicates that the system meets user needs in terms of usability, functionality, and accessibility. The implementation of this system contributes to improving academic information management and supports digital transformation at the Faculty of Law, Universitas Jambi.
Pengukuran Kepuasan Pengguna Website PTSP Kemenag Provinsi Jambi menggunakan Metode EUCS dan DeLone & McLean
The implementation of the One-Stop Integrated Service (PTSP) Website at the Ministry of Religious Affairs of Jambi City is part of the digital transformation of public services. This study aims to measure user satisfaction with the PTSP Website using the End User Computing Satisfaction (EUCS) method and the DeLone & McLean model. This research employed a quantitative approach using a survey method involving 100 respondents who are users of the PTSP Website. The EUCS variables consist of content, accuracy, format, ease of use, and timeliness, while the DeLone & McLean model includes system quality, information quality, and service quality. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0 software. The results indicate that system quality, information quality, and service quality have a positive effect on user satisfaction with the PTSP Website. This study is expected to serve as an evaluation reference for improving the quality of digital public services.
Analisis Sentimen Publik Terhadap Kebijakan Efisiensi Anggaran Menggunakan Naive Bayes, dan SVM
Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.
Perancangan UI/UX Smart Ordering System : Sistem Pemesanan Makanan Modern Berbasis Web
The development of information technology has driven digital transformation in various sectors, including the food and beverage (F&B) industry. However, many small to medium-scale F&B businesses still rely on manual ordering systems, resulting in long queues, order recording errors, limited menu information, and suboptimal user experience. This study aims to design the user interface (UI) and user experience (UX) of a web-based Smart Ordering System that provides convenience, efficiency, and comfort in the food ordering process. The research method used is the Design Thinking approach, which includes empathize, define, ideate, prototype, and testing stages. The design process involves user needs analysis, user flow development, wireframe creation, and high-fidelity prototype development using Figma. Usability testing is conducted using the Single Ease Question (SEQ) method to evaluate ease of use and user satisfaction. The results indicate that the proposed UI/UX design provides a clear ordering flow, intuitive interface, and easy-to-understand user experience. Based on the SEQ results, most users experienced no difficulty in using the system, indicating that the design meets usability criteria with a very good category and is suitable for implementation in the F&B industry.
Model Prediksi Pelunasan Haji Berbasis XGBoost Dengan Interpretasi Shap
This study develops an interpretable machine learning model to predict the settlement status of Hajj fees in Jambi Province, Indonesia. Utilizing the XGBoost algorithm on a dataset of 4,332 prospective pilgrims from 2025, the research addresses the critical challenge of class imbalance where only 28.5% of samples are labeled "Unsettled". The baseline XGBoost model achieved a ROC-AUC of 0.7778, with a recall of 0.3482 for the minority class. SHAP (SHapley Additive exPlanations) analysis was employed to interpret model predictions, revealing that financial features specifically NILAI_VA (Virtual Account Value), JML_SETORAN (Deposit Amount), and JML_PELUNASAN (Settlement Amount) are the most significant factors influencing repayment risk, with negative SHAP values indicating increased default probability. The findings demonstrate that an interpretable XGBoost framework can provide both predictive accuracy and actionable insights for policymakers, enabling targeted interventions such as flexible payment schemes and enhanced financial monitoring for high-risk pilgrims..
Implementasi YOLOv8 untuk Deteksi Pekerja Tanpa Safety Helmet dan Penghitung Durasi Pelanggaran di Area Industri Berbasis Real-Time
The use of protective equipment in the form of helmets is an important aspect of ensuring motorcycle rider safety. However, violations of helmet usage still frequently occur and are difficult to monitor continuously. This study proposes a real-time helmet detection system using the YOLOv8 object detection method. The YOLOv8n model was trained using a helmet and no-helmet image dataset that underwent data augmentation to improve the model’s robustness against variations in environmental conditions. The system was implemented using the Python programming language with the support of the Ultralytics and OpenCV libraries. The system input was obtained from a webcam with a resolution of 640×640 pixels, where each video frame was processed in real time to detect the Helmet and No Helmet classes. The system displays bounding boxes and class labels in real time and is equipped with a violation duration calculation mechanism. When a no-helmet condition is detected continuously, the system generates pop-up alerts and automatic notifications via the Telegram application. The experimental results show that the system is capable of detecting helmet usage and no-helmet violations in real time with stable performance. The integration of violation duration calculation helps reduce momentary detection errors and improves the reliability of identifying valid violations
Analisis Faktor-Faktor yang Mempengaruhi Upah Riil Tenaga Kerja Sektor Konstruksi Antar Provinsi di Indonesia Priode 2010-2023
This study aims to analyze the factors affecting real wages of construction workers across provinces in Indonesia from 2010 to 2023 using panel data analysis. The independent variables include Provincial Minimum Wage (UMP), Consumer Price Index (CPI), Open Unemployment Rate (TPT), and Performance Pay (Balas Jasa). A panel dataset of 476 observations from 34 provinces over 14 years was analyzed using three model approaches: Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM). The best model was determined through Chow Test, Hausman Test, and Lagrange Multiplier Test, which confirmed that the Fixed Effect Model (FEM) is the most appropriate for analyzing this research data. FEM estimation results show that simultneously, all independent variables (UMP, CPI, TPT, and Performance Pay) have a significant effect on real wages with an F-statistic value of 436,465.9 (p-value = 0.0000 < 0.05), indicating that the model as a whole is highly valid and capable of explaining the variation in real wages collectively. However, partial tests reveal that only the Real Wage variable has a positive and statistically significant effect on Performance Pay (coefficient = 106.3320; t-statistic = 1276.083; p-value = 0.0000), while UMP (p-value = 0.1472), CPI (p-value = 0.6460), and TPT (p-value = 0.6934) show no significant effects at the 5% significance level. The research model demonstrates very high predictive ability with an R-squared value of 0.999735 (99.97%), indicating that the variables studied can explain nearly all variation in real wages of construction workers at the provincial level. This research provides policy implications that improving real wages in the construction sector requires an integrated approach that focuses not only on minimum wage setting but also on regional inflation control, human capital quality improvement, and creating conducive labor market conditions through unemployment reduction
Perancangan UI/UX Pada Aplikasi Movement and Athletic Talent Hub (MATCH)
Conventional management of sports services often leads to inefficiency and limited public access to experts and facilities. Reliance on manual systems poses a high risk of scheduling conflicts or human error. This study aims to develop the User Interface (UI) and User Experience (UX) design for the Movement and Athletic Talent Hub (MATCH) application as an integrative digital solution. The approach employed is the Design Thinking method, encompassing the stages of empathize, define, ideate, prototype, and testing. The design process resulted in an interactive prototype featuring key functions such as facility booking, trainer search, and a digital payment system. Evaluation was conducted using the System Usability Scale (SUS) method involving target users. The test results yielded an average score of 79.5, categorizing the MATCH application within the Good rating and Acceptable status. These findings indicate that the design is effective in meeting user needs and is viable for further development as a digital sports ecosystem.
Analisis Pengaruh Kualitas Layanan terhadap Kepuasan Pengguna pada Website Dinas Kependudukan dan Pencatatan Sipil Kota Jambi Menggunakan Metode Webqual 4.0
Website Accessible Jambi City Population and Civil Registry Service https://disdukcapil.jambikota.go.id. The problem with the Jambi City Population and Civil Registry Service website is that not all information is available, especially on the Profile menu there is a Media Information sub-menu, Data menu and Facilities and Infrastructure menu. On the Information Media sub-menu, there is a Demographic Data sub-menu, where the contents of the sub-menu are still empty, preventing users from obtaining information. On the Public Facilities and Infrastructure menu from the sub menu, the data cannot be accessed so that it makes users unable to get information. And in the appearance of the Jambi City Population and Civil Registry Service, when accessed via Google Chrome, the appearance is disorganized, so users have to open the website using a laptop/PC to get a website display that is orderly and easy for users to understand. Quality measurement is carried out based on user satisfaction point of view in order to improve the quality of service to the community and make optimal use of the website. In analyzing user satisfactionwebsite DUKCAPIL Jambi using the webqual 4.0 method, there are 4 variables, namely usability (usability), information quality (information quality), interaction quality (interaction quality), and user satisfaction (user satisfaction) and using the software (software) SPSS. Of the 3 hypotheses proposed, all hypotheses were accepted in this study.