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All Journal Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Nabil, Avrillistianto Ananda
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Humanities Education Languange, Linguistic, Communication & Media Mathematics Other

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Klasifikasi Hoax Menggunakan Metode TF-IDF + SVM: Penelitian Nabil, Avrillistianto Ananda; Wildantama, Farih Ramdan; Satrianto, Dimas; Bakara, Michael Gilbert; Budiawan, Imam; Mulyati, Desi
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5078

Abstract

The spread of hoax news on social media causes social unrest and economic losses. This study builds a classification model for Indonesian hoax news using Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine (SVM). The dataset consists of 970 news from TurnBackHoax.id with FALSE and FRAUD categories. The research includes text preprocessing, TF-IDF feature extraction with unigram and bigram, and linear kernel SVM classification. Data was split 80:20 using stratified sampling with parameter optimization through Grid Search and 5-fold Cross Validation. Evaluation results show the model classifies hoax news with good performance based on accuracy, precision, recall, and f1-score metrics. The confusion matrix indicates most data was correctly classified despite errors in news with overlapping linguistic patterns. The study proves TF-IDF and SVM combination is effective for Indonesian hoax detection with low computational requirements. Further development is recommended using larger datasets and comparing with deep learning methods.
Co-Authors Bakara, Michael Gilbert Desi Mulyati Imam Budiawan Satrianto, Dimas Wildantama, Farih Ramdan