<![CDATA[Purpose – This study addresses the escalation of disinformation during the 2024 Indonesian General Election by developing an automated hoax detection system. The primary focus is to evaluate the integration of data balancing methods to minimize detection failures in hoax narratives, which often appear less frequently than factual news in real-world scenarios. Methods – The dataset consists of 1,529 unique news documents, comprising 1,020 factual articles from Kompas.com and 509 hoax articles from TurnBackHoax.id. The modelling workflow involves text preprocessing, feature extraction via Term Frequency-Inverse Document Frequency (TF-IDF), and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The model was developed using the Multinomial Naive Bayes algorithm and integrated into a web-based platform using the Flask framework. Findings – Results from 5-fold cross-validation demonstrated a stable average accuracy of 92.64% (±1.27%). On an independent test set, the model achieved 89.54% accuracy with a hoax class recall of 0.93. This proves that SMOTE significantly enhances sensitivity in identifying disinformation, reducing the risk of false information bypassing the system. Research implications – The study is limited to textual data from curated sources and cannot yet capture multimedia disinformation or high levels of sarcasm. Future research should explore Transformer-based models for deeper semantic context understanding. Originality – This research provides a functional public verification platform with a response time of under one second. Its novelty lies in a domain-specific detection system that balances algorithmic efficiency with precision on imbalanced real-world election data.]]>
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