Klasifikasi Hoax Menggunakan Metode TF-IDF + SVM: Penelitian

Avrillistianto Ananda Nabil; Farih Ramdan Wildantama; Dimas Satrianto; Michael Gilbert Bakara; Imam Budiawan; Desi Mulyati

doi:10.31004/jerkin.v4i3.5078

Authors

Avrillistianto Ananda Nabil Universitas Bina Sarana Informatika
Farih Ramdan Wildantama Universitas Bina Sarana Informatika
Dimas Satrianto Universitas Bina Sarana Informatika
Michael Gilbert Bakara Universitas Bina Sarana Informatika
Imam Budiawan Universitas Bina Sarana Informatika
Desi Mulyati Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.31004/jerkin.v4i3.5078

Keywords:

Hoax News, TF-IDF, Support Vector Machine, Text Mining, Hoax Detection

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.

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