Political Comperative Analysis of Indonesian Political Fake News Detection using IndoBERT-Bi-GRU-Attention Models: Evaluating Performance on Narratives and News Headlines Datasets - Manurung, Juliana Damayanti; Purba, Ronsen (Building of Informatics, Technology and Science (BITS), 2025)
The instant and massive spread of fake news on social media negatively impacts public trust in the media and news agencies. In politics, fake news is often used by politicians to gain support ahead of elections. Detecting fake news in Indonesia poses a significant challenge, especially for communities vulnerable to misinformation. This study aims to develop a new model that combines IndoBERT with Bi-GRU and Attention. Additionally, a comparison is made between the main model and two word embedding models, FastText and GloVe. The tests were conducted on datasets of headlines and news narratives separately. Data was sourced from CNN, Tempo.co, Kompas, and TurnBackHoax.ID. The results show that the IndoBERT-Bi-GRU-Attention model with FastText excelled on the headline dataset with an accuracy of 99.76% and an F1-Score of 99.61%, while the main IndoBERT-Bi-GRU-Attention model excelled on the narrative dataset with an accuracy of 99.08% and an F1-Score of 98.40%. This research demonstrates that IndoBERT can be combined with Bi-GRU, significantly contributing to the development of fake news detection models.