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All Journal Journal of Mathematics, Computation and Statistics (JMATHCOS)
Rahmat H. S.
Universitas Negeri Makassar
Mathematics

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Implementation of DBSCAN for Earthquake Clustering in Indonesia with Potential Surface Damage Hardianti Hafid; Rahmat Hidayat; Rahmat H. S.
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.7318

Abstract

Indonesia is one of the countries with the highest seismic activity in the world due to its location at the convergence of three major tectonic plates. Understanding earthquake distribution patterns is crucial for disaster mitigation efforts and policy planning. This study applies the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster earthquake data in Indonesia based on magnitude and depth. The data used is secondary data from the Meteorology, Climatology, and Geophysics Agency (BMKG) for the period of January–December 2023. The research stages include data collection and preprocessing, applying the DBSCAN algorithm with the selection of Eps and MinPts parameters, and evaluating the clustering results using the silhouette coefficient and Davies-Bouldin Index (DBI). The results show that the combination of Eps = 0.5 and MinPts = 5 produces clusters with a silhouette coefficient of 0.3959 and a DBI of 0.7384, indicating a fairly good cluster structure. Visualization results reveal high-density clusters in active seismic zones and several smaller clusters representing specific earthquake characteristics. This study provides insights into the earthquake distribution patterns in Indonesia and demonstrates that DBSCAN effectively identifies complex cluster structures. The findings can serve as a reference for seismological studies and support earthquake disaster mitigation efforts.
Spatial Dynamics of Digital Society Index in Indonesia: A Spatial Autoregressive Approach Zakiyah Mar'ah; Rahmat H. S.; Rahmat Hidayat
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9217

Abstract

The equitable distribution of digital transformation in Indonesia is a strategic issue, given the importance of technology in supporting national development. Indeks Masyarakat Digital Indonesia (IDSI) or Indonesian Digital Society Index (IDSI) was developed to measure the level of digital literacy, inclusion, and digital infrastructure readiness at the provincial level. However, the distribution of IDSI values shows striking spatial disparities. This research aims to identify spatial dependency patterns and significant factors influencing IDSI using a Spatial Autoregressive (SAR) model. The data used is the 2024 IDSI from 34 provinces in Indonesia, with five independent variables: access and adoption of digital technology, learning ecosystem, ICT introduction, empowerment, and employment. A spatial autocorrelation test using Moran's I revealed a significant positive spatial dependency, indicating a clustered pattern in IDSI distribution. The Lagrange Multiplier test showed spatial dependency in the lag or response variable, making the SAR model suitable. Estimation results demonstrate that all five independent variables significantly impact IDSI, with a coefficient of determination (R²) of 0.98. These findings indicate that geographically proximate regions tend to have similar IDSI values. Therefore, spatial approaches like SAR are crucial for formulating national digital equity policies.
Co-Authors Hardianti Hafid Rahmat Hidayat Zakiyah Mar'ah