<![CDATA[Indonesia, located along the Pacific Ring of Fire, experiences high seismic activity with over 6,000 earthquakes annually. Accurate earthquake prediction remains a major challenge because of the complexity of geological dynamics and limitations of traditional methods in capturing nonlinear seismic patterns. Although deep learning approaches have shown promise, previous studies have often treated spatial and temporal analyses separately, limiting holistic predictive performance. This study proposes a novel hybrid CNN-GRU deep learning model that integrates spatial feature extraction CNN and temporal sequence modeling GRU, and compares its performance with of that CNN, LSTM, GRU, and Bidirectional LSTM using a dataset of 117,251 earthquake events in Indonesia (2008–2024). The results show that Bidirectional LSTM achieved the best temporal accuracy (R² 0.653, RMSE 0.592), while the hybrid CNN-GRU provided balanced spatial-temporal performance (R² 0.587). Notably, the performance gap between Bidirectional LSTM and other models (e.g., Hybrid CNN-GRU) was statistically validated via paired t-test (p < 0.05). The proposed models generalize well to unseen regions such as Maluku-Papua. The key contribution is the hybridization of spatial-temporal learning in a single-model architecture - where CNN processes latitude-longitude coordinates via 1D convolutions while GRU handles temporal sequences - an approach lacking in earlier works. This directly improves early warning systems in seismically active areas by providing 32% higher accuracy than conventional methods.]]>
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