Subduction and Local Fault Earthquake Analysis Using ST-DBSCAN Clustering Algorithm in The Special Region of Yogyakarta (DIY)

This study aims to analyze the spatio-temporal patterns of subduction and local fault earthquakes in the Special Region of Yogyakarta using the ST-DBSCAN (Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise) algorithm. A total of 5,403 earthquake events from 2019 to 2024 were clustered using spatial parameters (2–5 km) and a temporal window of 10 days. The results were evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. In the subduction zone, nine clusters were identified with a DBI of 3.23 and a Silhouette Score of 0.18, indicating moderate separation. Meanwhile, 25 clusters were detected in the local fault zone, particularly around the Opak and Oyo Faults, with a higher DBI of 3.82 and a negative Silhouette Score (-0.14), suggesting overlapping clusters and weak structure. The clustering outcomes correlate with geological features and offer insights for improving earthquake hazard assessment and early warning systems in Yogyakarta.

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Publication Details

Journal
Kaunia Integration and Interconnection Islam and Science
Published
2025-08-22
DOI
https://doi.org/10.14421/kaunia.5347
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Subduction and Local Fault Earthquake Analysis Using ST-DBSCAN Clustering Algorithm in The Special Region of Yogyakarta (DIY)

Nugroho Budi Wibowo, Irfan Pratama, Wuri Handayani
Kaunia Integration and Interconnection Islam and Science
Data Mining and Machine Learning Applications
article

Subduction and Local Fault Earthquake Analysis Using ST-DBSCAN Clustering Algorithm in The Special Region of Yogyakarta (DIY)

Nugroho Budi Wibowo, Irfan Pratama, Wuri Handayani
article en

Abstract

This study aims to analyze the spatio-temporal patterns of subduction and local fault earthquakes in the Special Region of Yogyakarta using the ST-DBSCAN (Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise) algorithm. A total of 5,403 earthquake events from 2019 to 2024 were clustered using spatial parameters (2–5 km) and a temporal window of 10 days. The results were evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. In the subduction zone, nine clusters were identified with a DBI of 3.23 and a Silhouette Score of 0.18, indicating moderate separation. Meanwhile, 25 clusters were detected in the local fault zone, particularly around the Opak and Oyo Faults, with a higher DBI of 3.82 and a negative Silhouette Score (-0.14), suggesting overlapping clusters and weak structure. The clustering outcomes correlate with geological features and offer insights for improving earthquake hazard assessment and early warning systems in Yogyakarta.

Kaunia Integration and Interconnection Islam and ScienceVol. 21(1)
Universitas Mercu Buana Yogyakarta (ID), Universitas Teknologi Yogyakarta (ID)
Universitas Mercu Buana
Openalex Percentile: Top 67%
Data Mining and Machine Learning Applications
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