<![CDATA[The spread of hoaxes in digital media poses a major challenge for automated detection systems as language and topics evolve over time. Although Transformer-based models such as IndoBERT have demonstrated high accuracy in previous studies, their performance across different time periods remains underexplored. This study examines the cross-temporal generalization ability of IndoBERT for hoax news classification. The model was trained on labeled articles from 2018–2023 and tested on data from 2025 to evaluate its robustness against temporal distribution shifts. The results indicate high accuracy on similar-period data (99.67–99.89%) but a decrease on 2025 data (95.45–95.87%), with most errors occurring as false negatives in the hoax class. These findings highlight the impact of temporal distribution shifts on model reliability and underscore the importance of adaptive strategies such as periodic retraining and domain-based data augmentation. Practically, this model has the potential to assist social media platforms and government institutions in developing dynamic and time-adaptive hoax detection systems. The cross-temporal approach employed in this study also offers methodological innovation compared to conventional random validation, as it better reflects real-world conditions where misinformation patterns continually evolve.]]>
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