LSTM-Based Deep Learning Approach for Hoax Detection on Indonesian Social Media - Nuraisana nuraisana; R. Mahdalena Simanjorang; Thania Rizky Ristanti; Della Triyani (The IJICS (International Journal of Informatics and Computer Science), 2026)
The widespread adoption of social media, particularly Facebook, has drastically accelerated information dissemination while simultaneously amplifying the spread of misinformation. Hoax content poses a serious threat to public trust and social cohesion. This study proposes a hoax classification system based on the Long Short-Term Memory (LSTM) architecture, selected for its inherent ability to model sequential dependencies in textual data. The experimental workflow covered text preprocessing (case folding, tokenization, stopword removal, and stemming), word vectorization using the Keras Tokenizer, and end-to-end LSTM model training on the “Indonesian Fact and Hoax Political News” dataset from Kaggle, totaling 4,502 samples with a balanced 80:20 train-test split. Experimental results demonstrate the proposed model achieves 92.44% accuracy, 92.63% precision, 92.22% recall, and a 92.42% F1-score on the test set. These findings confirm that LSTM effectively captures contextual and sequential linguistic patterns in Indonesian-language content, offering a viable and scalable solution for automated hoax detection on social media platforms.