Deteksi Berita Hoax Berbahasa Indonesia Menggunakan Multilingual-BERT pada Media Berita Online - Syalma Tarissa; Muhamad Akbar; A. Taqwa Martadinata; Joni Karman (TIN: Terapan Informatika Nusantara, 2026)
The development of infoormation technology is increasing, which can trigger the spread of hoaxes in online newss media, resulting in misinformation for the public. Manual detection of hoaxes is difficult due to the large volume of online news and variety of language styles and mixed language usage. Advances in artificial intelligence technology, particularly Natural Language Processing (NLP), open up significant opportunities for automatically detecting hoax news. Transformer-based NLP models, such as Multilingual-BERT (mBERT), are emerging. Transformer-based models such as Multilngual-BERT (mBERT) are capable of understanding text context in both directions and support various languages, including Indonesian. Therefore, this study aims to apply and test the effectiveness of mBERT in detecting hoax news in online news media. The dataset used comes from TurnbackHoax on the Kaggle website for hoax news, Tempo and CNN Indonesia for non-hoax news datasets, and additional data from the Kaggle website on news coverage in 2025. The data was processed using the built-in mBERT Tokenizer with data undersampling techniques, resulting in a model with an accuracy of 0.97, a precision value of 0.96, a recall of 0.97, and F1-score of 0.97,which shows that the mBERT approach is able to provide high classification performance on the Indonesian language hoax news dataset.