<![CDATA[East Java is one of the seismically active regions in Indonesia, yet predictive studies that integrate spatial data and event parameters remain limited. This study develops a two-stage approach to model earthquake risk more comprehensively by combining Bayesian inference and logistic regression. The first stage employs a Bayesian model to estimate the daily probability of earthquake occurrence based on historical data from 2014 to 2024. The results show an average daily probability of 13.5%, with a 95% credible interval indicating a high level of confidence. Spatially, Region 1 (covering southern East Java) is identified as the area with the highest probability, followed by Region 3 and Region 2. In the second stage, logistic regression is used to identify combinations of event parameters—particularly magnitude and depth—that significantly influence the likelihood of moderate-to-major earthquakes (magnitude ≥ 5.0). The prediction results indicate that most high-risk events occur at shallow depths in Region 1 and Region 3, while Region 2 appears less frequently but still presents underlying geological hazards. These findings demonstrate that integrating probabilistic modeling with parameter-based classification offers a more refined understanding of earthquake risk. As an initial framework, this study also opens avenues for developing future early warning systems based on dynamic data and machine learning methods.]]>
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