<![CDATA[Earthquakes pose a major threat in Indonesia, particularly in complex tectonic regions like Sulawesi and Maluku. To support disaster mitigation, this research employs the Self Organizing Maps (SOM) method—an unsupervised technique that reduces data dimensionality into an intuitive two-dimensional form—to cluster earthquake data using four key variables: longitude, latitude, magnitude, and depth. The dataset includes 5,275 earthquake records from 2022, sourced from the Meteorology, Climatology, and Geophysics Agency (BMKG). SOM training produced 25 neurons, which were then grouped into three optimal clusters using hierarchical clustering, validated by internal metrics: the lowest Connectivity Index (296.1512), highest Silhouette Index (0.3304), and a Dunn Index of 0.0058. Cluster 1, with 13 neurons, covers eastern Sulawesi and Maluku, featuring medium magnitude and depth. Cluster 2, with 11 neurons, represents central to southern Sulawesi, characterized by low magnitude and shallow depth. Cluster 3, comprising a single neuron, includes western regions with high-magnitude, very deep earthquakes. Keywords⎯ Clustering, Earthquake, Internal Validation, Self Organizing Maps (SOM).]]>
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