Klasifikasi Berita Hoaks Menggunakan Algoritma Support Vector Machine
DOI:
https://doi.org/10.61132/prosemnasproit.v2i2.201Keywords:
Berita Hoaks, Text Mining, Support Vector Machine, TF-IDF, Google ColabAbstract
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.
References
Aggarwal, C. C., & Zhai, C. (2012). Mining text data. Springer.
Asistyasari, A., Sudarsono, B., & Faddilah, U. (2021). Klasifikasi Berita Hoaks Menggunakan Algoritma Vector Space Model Classification of Hoax News Using the Vector Space Model Algorithm. 2, 112–119. https://doi.org/10.37373/infotech.v2i2.176
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Feldman, R., & Sanger, J. (2007). The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press.
Ilmiah, J., & Pendidikan, W. (2022). 3 1,2,3. 8(September), 11–20.
Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. Proceedings of the European Conference on Machine Learning, 137–142.
Komdigi Republik Indonesia. (2024). Klarifikasi Berita Hoaks. https://www.komdigi.go.id
Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31(3), 249–268.
Liu, B. (2011). Web data mining: Exploring hyperlinks, contents, and usage data. Springer.
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.
Of, C., News, H., To, R., General, T. H. E., & Of, E. (2024). KLASIFIKASI BERITA HOAX TERKAIT PEMILIHAN UMUM PRESIDEN REPUBLIK INDONESIA TAHUN 2024 MENGGUNAKAN NAÏVE BAYES DAN SVM CLASSIFICATION OF HOAX NEWS RELATED TO THE GENERAL ELECTION OF THE PRESIDENT OF THE REPUBLIC OF INDONESIA IN 2024 USING NAÏVE BAYES AND SVM. 20.
R. R. Sani, Y. A. Pratiwi, S. Winarno, E. D. Udayanti, and F. Alzami, "Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia," Jurnal Masyarakat Informatika, vol. 13, no. 2, pp. 85 - 98, Nov. 2022. https://doi.org/10.14710/jmasif.13.2.47983
Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1–47.
Tan, P. N., Steinbach, M., & Kumar, V. (2019). Introduction to data mining (2nd ed.). Pearson.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Data mining: Practical machine learning tools and techniques (4th ed.). Morgan Kaufmann.
Zhang, Y., & Wallace, B. (2015). A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. Proceedings of the Eighth International Joint Conference on Natural Language Processing, 253–263.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Prosiding Seminar Nasional Ilmu Teknik
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
