Klasterisasi Pola Seismik: Perbandingan K-Means dan ST-DBSCAN pada Data Gempa Spasio-Temporal
-
Muhammad Putra Pratama Budiman
*
Universitas Teknologi Yogyakarta, Daerah Istimewa Yogyakarta,
Indonesia
- Muhammad Zakariyah Universitas Teknologi Yogyakarta, Daerah Istimewa Yogyakarta, Indonesia (*) Corresponding Author
Abstract
Earthquakes are natural disasters with significant socio-economic impacts; thus, understanding their distribution patterns and characteristics is essential for disaster mitigation. Indonesia is situated along the Pacific Ring of Fire with intense seismic activity, and the volume of earthquake data recorded by BMKG is too vast for manual analysis. This study compares K-Means and ST-DBSCAN using 93,815 spatio-temporal data points from a historical catalog (2008–2023) and data from the final 30 days of 2026. The number of K-Means clusters was determined using the elbow method supported by the Silhouette Score and Davies-Bouldin Index (DBI); both algorithms were evaluated using the Silhouette Score, DBI, and Adjusted Rand Index (ARI) across six tectonic zones. K-Means (k = 4) outperformed ST-DBSCAN across all three metrics (Silhouette Score: 0.1563, DBI: 2.7180, ARI: 0.4137), whereas ST-DBSCAN produced 20 clusters with a single cluster containing 70.9% of the data. Three K-Means clusters corresponded to the Sulawesi–Maluku, Sunda Arc, and Sumatra regions, while one represented a temporal artifact from the time_days feature. An ablation test excluding time_days (k = 3) improved the ARI to 0.5102. Results are presented via the Flask-based NusaSeis web dashboard with interactive maps and automated interpretations by Gemini 2.5 Flash; all 23 black box scenarios were validated. NusaSeis serves as an open seismic exploration platform enabling academics and disaster management agencies to interactively access national seismicity summaries without programming expertise, though it is not yet suitable for official tectonic zoning or hazard assessment.
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