Fake News Detection in Indonesian Language Using IndoBERT with LIME-Based Keyword Interpretation

Fake news dissemination in the digital era has become a serious issue, particularly in political and public information domains. This study proposes an Indonesian fake news detection system that uses the IndoBERT transformer model, combined with LIME (Local Interpretable Model-Agnostic Explanations), for keyword-based interpretation. The primary objective of this study is not only to achieve high classification performance but also to enhance model transparency by identifying the most influential words contributing to prediction results. This study follows the SEMMA (Sample, Explore, Modify, Model, Assess) methodology, starting with dataset collection, exploratory data analysis, text preprocessing, model fine-tuning, and evaluation, and concluding with interpretability analysis using LIME. The dataset consists of 31,310 Indonesian political news articles categorized into hoax and factual classes. IndoBERT is fine-tuned using the Hugging Face framework with optimized hyperparameters and class weighting to address class imbalance. Experimental results show that the proposed model achieves an accuracy of 99.78%, precision of 99.81%, recall of 99.52%, and F1-score of 99.66%, demonstrating strong performance in distinguishing hoax and factual news. Furthermore, LIME-based analysis provides interpretable insights by highlighting keywords that influence model predictions, thereby improving transparency and user trust. Words associated with conspiracy and unverified claims contribute strongly to the hoax class, while terms related to official institutions and statistical information support factual classification. The results indicate that integrating IndoBERT with LIME not only improves classification performance but also enhances explainability in Indonesian fake news detection systems.