Identifikasi Berita Palsu di Portal Media Online Menggunakan Model IndoBERT dan LSTM - Kamal, Angga Mochamad; Chrisnanto, Yulison Herry; Yuniarti, Rezki (Universitas Budi Darma, 2025)
The rapid spread of political fake news on Indonesian online media portals poses serious threats to public trust and democratic stability. The main research problem is the limitation of existing models in handling the complexity of Indonesian political narratives containing local idioms and long text structures. The proposed solution employs a hybrid IndoBERT-LSTM model with ensemble stacking approach using logistic regression meta-learner to optimize fake news detection. IndoBERT is selected to capture Indonesian language nuances, while LSTM handles sequential dependencies in long articles. The research objective is to develop an accurate detection system for political fake news by leveraging the complementary strengths of both models. The dataset comprises 32,218 political articles from credible portals (Kompas, CNN Indonesia, Tempo, Detiknews, Viva) and Turnbackhoax.id validation from September 2021 to December 2024. Research results demonstrate that ensemble stacking achieves superior performance with F1-score 0.9544, accuracy 95.41%, and AUC-ROC 0.9936, outperforming standalone IndoBERT (F1: 0.9542) and LSTM (F1: 0.9417). Error analysis identifies 4.59% error rate with 134 false positives and 88 false negatives, particularly in long articles (average 2,739 characters). This model has potential for integration into fact-checking platforms for real-time detection of Indonesian political fake news.