SISTEM DETEKSI BERITA PALSU DUA BAHASA MENGGUNAKAN TF-IDF DAN MULTINOMIAL NAIVE BAYES - Rheno Septianto; Yan Rianto (JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya), 2026)
The rapid spread of misinformation poses a major threat to public trust and digital literacy. This study develops a bilingual fake news detection system capable of analyzing news content in English and Indonesian. The system uses two separate monolingual models trained independently on the WELFake dataset (English) and the Berita Hoax 2023 dataset (Indonesian). Each model applies text preprocessing techniques such as tokenization, stopword removal, and normalization before transforming the text using TF-IDF. The classification process utilizes the Multinomial Naïve Bayes algorithm, chosen for its efficiency in handling high-dimensional text data. The bilingual system integrates an automatic language detection module that selects the appropriate model based on the detected language. Evaluation results show that the English model achieves an accuracy of 86%, while the Indonesian model achieves an accuracy of 93%. These results indicate that the two-model bilingual approach provides reliable performance for multilingual fake news detection. This study contributes to practical solutions for misinformation mitigation, especially in multilingual environments like Indonesia.