Komparasi Algoritma SVM dan Random Forest Dalam Sentimen Analisis Review Shopee di Google Play Store Dengan Anova

Eko Susanto; Sharipuddin Sharipuddin; Benni Purnama

doi:10.61132/prosemnasproit.v2i2.177

Authors

Eko Susanto Universitas Dinamika Bangsa
Sharipuddin Sharipuddin Universitas Dinamika Bangsa
Benni Purnama Universitas Dinamika Bangsa

DOI:

https://doi.org/10.61132/prosemnasproit.v2i2.177

Keywords:

ANOVA, Google Play Store, Random Forest, sentiment analysis, SVM

Abstract

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.

References

Alassaf, M., & Qamar, A. M. (2022). Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA. Journal of King Saud University - Computer and Information Sciences, 34(6), 2849–2859. https://doi.org/10.1016/j.jksuci.2020.10.023

AlBadani, B., Shi, R., & Dong, J. (2022). A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM. Applied System Innovation, 5(1), 13. https://doi.org/10.3390/asi5010013

Ali, M. M., Islam, M. S., Uddin, M. N., & Uddin, Md. A. (2024). A conceptual IoT framework based on Anova-F feature selection for chronic kidney disease detection using deep learning approach. Intelligence-Based Medicine, 10, 100170. https://doi.org/10.1016/j.ibmed.2024.100170

Arioz, U., Smrke, U., Plohl, N., & Mlakar, I. (2022). Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models. Diagnostics, 12(11), 2683. https://doi.org/10.3390/diagnostics12112683

Azzahra, N., Murdiansyah, D., & Lhaksmana, K. (2021). Toxic Comment Classification on Social Media Using Support Vector Machine and Chi Square Feature Selection. International Journal on Information and Communication Technology (IJoICT), 7(1), 64–76. https://doi.org/10.21108/ijoict.v7i1.552

Borbon, M. R., & Ebardo, R. A. (2025). Social media discussions on educators: Selecting and appraisal of recent research using TF-IDF. Computers and Education Open, 9, 100293. https://doi.org/10.1016/j.caeo.2025.100293

Contreras, D., Wilkinson, S., Aktas, Y. D., Fallou, L., Bossu, R., & Landès, M. (2022). Intensity-Based Sentiment and Topic Analysis. The Case of the 2020 Aegean Earthquake. Frontiers in Built Environment, 8. https://doi.org/10.3389/fbuil.2022.839770

Guido, R., Ferrisi, S., Lofaro, D., & Conforti, D. (2024). An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review. Information, 15(4), 235. https://doi.org/10.3390/info15040235

HaCohen-Kerner, Y., Miller, D., & Yigal, Y. (2020). The influence of preprocessing on text classification using a bag-of-words representation. PLOS ONE, 15(5), e0232525. https://doi.org/10.1371/journal.pone.0232525

Hassan, S. U., Ahamed, J., & Ahmad, K. (2022). Analytics of machine learning-based algorithms for text classification. Sustainable Operations and Computers, 3, 238–248. https://doi.org/10.1016/j.susoc.2022.03.001

Huang, H., Zavareh, A. A., & Mustafa, M. B. (2023). Sentiment Analysis in E-Commerce Platforms: A Review of Current Techniques and Future Directions. IEEE Access, 11, 90367–90382. https://doi.org/10.1109/ACCESS.2023.3307308

Kaesmetan, Y. R., & Kalengkongan, W. W. (n.d.). Classification of Public Sentiment Related to Stunting in Indonesia Using BERT and SVM. 8(2), 11–33. https://doi.org/10.23965/jbase.v8i2.8960

Kaur, G., & Sharma, A. (2023). A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis. Journal of Big Data, 10(1). https://doi.org/10.1186/s40537-022-00680-6

Kemala, B. K. L., Surjandari, I., & Puspita, A. N. G. (2024). Forecasting Methods for the Electric Vehicle Ownership: A Literature Review. Procedia Computer Science, 234, 87–95. https://doi.org/10.1016/j.procs.2024.02.155

Kumari, A., & Singh, J. (2025). Designing of guava quality classification model based on ANOVA and machine learning. Scientific Reports, 15(1), 33920. https://doi.org/10.1038/s41598-025-09684-7

Lloyd, R., Munro, J., Evans, K., Gaskin-Williams, A., . Hui, A., Pearson, M., Slade, M., Kotera, Y., Day, G., Loughlin-Ridley, J., Enston, C., & Rennick-Egglestone, S. (2023). Health service improvement using positive patient feedback: Systematic scoping review. PLOS ONE, 18(10), e0275045. https://doi.org/10.1371/journal.pone.0275045

Mamdouh Farghaly, H., & Abd El-Hafeez, T. (2023). A high-quality feature selection method based on frequent and correlated items for text classification. Soft Computing, 27(16), 11259–11274. https://doi.org/10.1007/s00500-023-08587-x

Molero, J. M., Pérez-Martín, J., Rodrigo, A., & Peñas, A. (2023). Offensive Language Detection in Spanish Social Media: Testing From Bag-of-Words to Transformers Models. IEEE Access, 11, 95639–95652. https://doi.org/10.1109/ACCESS.2023.3310244

Muflikhah, L., Iskandar, A., Yudistira, N., Nadlori, I. U., & Dewanto, B. N. (n.d.). High performance of Dengue shock syndrome detection using extreme gradient boosting with ANOVA feature selection. Journal of Biotech Research.

Nasiri, M., & Rahmani, H. (2021). DENOVA: Predicting Five-Factor Model using Deep Learning based on ANOVA. Technology Journal of Artificial Intelligence and Data Mining, 9(4), 451–463. https://doi.org/10.22044/JADM.2021.10471.2186

Nida Nur Aini Aryanti, & Ozzi Suria. (2025). ANALISIS SENTIMEN TERHADAP PEMUTUSAN HUBUNGAN KERJA DI INDONESIA : KOMPARASI INDOBERT DENGAN SVM, RANDOM FOREST, DAN DECISION TREE DENGAN OPTIMASI TF - IDF. Rabit : Jurnal Teknologi Dan Sistem Informasi Univrab, 10(2), 1158–1176. https://doi.org/10.36341/rabit.v10i2.6364

Ningrum, A. F., & Amri, I. F. (2024). Sentiment Analysis Of Public Opinion On Handling Stunting In Indonesia Using Random Forest Ariska Fitriyana Ningrum (1) , Ihsan Fathoni Amri (2). In J Statistika (Vol. 17, Issue 1).

Nurrahma, & Yusuf, R. (2020). Comparing Different Supervised Machine Learning Accuracy on Analyzing COVID-19 Data using ANOVA Test. 2020 6th International Conference on Interactive Digital Media (ICIDM), 1–6. https://doi.org/10.1109/ICIDM51048.2020.9339676

Ramadhona Hassolthine, C., Haryanto, T., Adline Twince Tobing, F., & Ikhwani Saputra, M. (2025). E-Commerce Product Review Sentiment Analysis: A Comparative Study of Naïve Bayes Classifier and Random Forest Algorithms on Marketplace Platforms. International Journal of New Media Technology, 12(1), 55.

Ramasamy, L. K., Kadry, S., Nam, Y., & Meqdad, M. N. (2021). Performance analysis of sentiments in Twitter dataset using SVM models. International Journal of Electrical and Computer Engineering (IJECE), 11(3), 2275. https://doi.org/10.11591/ijece.v11i3.pp2275-2284

Roussinov, D., Conkie, A., Patterson, A., & Sainsbury, C. (2022). Predicting Clinical Events Based on Raw Text: From Bag-of-Words to Attention-Based Transformers. Frontiers in Digital Health, 3. https://doi.org/10.3389/fdgth.2021.810260

Scientifico Disciplinare, S. (2023). DOTTORATO DI RICERCA IN MODELLI E METODI PER L’ECONOMIA E IL MANAGEMENT (ANALYTICS FOR ECONOMICS AND MANAGEMENT) Statistical Analysis of Grouped Text Documents.

Setiawan, A., Firman, ), Hasan, N., & Artikel, R. (2025). ANALISIS SENTIMEN TANGGAPAN PENGGUNA APLIKASI BALE BY BTN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) 1) INFO ARTIKEL ABSTRAK. 4(4), 315–326. https://doi.org/10.55123

Stefanis, C., Giorgi, E., Kalentzis, K., Tselemponis, A., Nena, E., Tsigalou, C., Kontogiorgis, C., Kourkoutas, Y., Chatzak, E., Dokas, I., Constantinidis, T., & Bezirtzoglou, E. (2023). Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1191730

Subarkah, P., Rahayu, P. W., Darmayanti, I., & Riyanto, R. (2023). SENTIMENT ANALYSIS ON REVIEWS OF WOMEN’S TOPS ON SHOPEE MARKETPLACE USING NAIVE BAYES ALGORITHM. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 9(1), 126–133. https://doi.org/10.33480/jitk.v9i1.4179

Sumertajaya, I. M., Angraini, Y., Harahap, J. R., & Fitrianto, A. (2022). Sentiment Analysis on Covid-19 Vaccination in Indonesia Using Support Vector Machine and Random Forest. JUITA: Jurnal Informatika, 10(1), 1. https://doi.org/10.30595/juita.v10i1.12394

Tan, K. L., Lee, C. P., & Lim, K. M. (2023). A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research. Applied Sciences, 13(7), 4550. https://doi.org/10.3390/app13074550

Wilk-Jakubowski, J. L., Pawlik, Ł., Ciopiński, L., & Wilk-Jakubowski, G. (2025). Synergistic Computing for Sustainable Energy Systems: A Review of Genetic Algorithm-Enhanced Approaches in Hydrogen, Wind, Solar, and Bioenergy Applications. Energies, 18(22), 6027. https://doi.org/10.3390/en18226027

Yang, J., & Chew, E. (2021). A Systematic Review for Service Humanoid Robotics Model in Hospitality. International Journal of Social Robotics, 13(6), 1397–1410. https://doi.org/10.1007/s12369-020-00724-y

Yusuf, A. A. (2021). The impact of Industry 4.0 on the Indonesian economy: A general equilibrium assessment. Regional Science Policy & Practice, 13(6), 1805–1825. https://doi.org/10.1111/rsp3.12463