Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms
Indonesia’s frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, Indonesia's frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, necessitate precise seismic zone identification to improve disaster preparedness. Thi...
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Ikatan Ahli Informatika Indonesia
2024-12-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5514 |
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| author | Nurfidah Dwitiyanti Siti Ayu Kumala Shinta Dwi Handayani |
| author_facet | Nurfidah Dwitiyanti Siti Ayu Kumala Shinta Dwi Handayani |
| author_sort | Nurfidah Dwitiyanti |
| collection | DOAJ |
| description | Indonesia’s frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, Indonesia's frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, necessitate precise seismic zone identification to improve disaster preparedness. This research evaluates the effectiveness of five clustering algorithms—K-Medoids, K-Means, DBSCAN, Fuzzy C-Means, and K-Affinity Propagation (K-AP)—for analyzing earthquake data from January 2017 to January 2023. Using a dataset from BMKG encompassing 13,860 seismic events, each algorithm was assessed based on Silhouette Score and Cluster Purity metrics. Results indicated that K-Means provided the best balance, forming six clusters with a Silhouette Score of 0.3245 and Cluster Purity of 0.7366, making it the most suitable for seismic zone analysis. K-Medoids closely followed with a Silhouette Score of 0.3158 and Cluster Purity of 0.7190. Although DBSCAN effectively handled noise, its negative Silhouette values indicated poor clustering quality. Fuzzy C-Means and K-AP underperformed, with K-AP generating an impractically high number of clusters (196) and the lowest Silhouette Score (0.2550). This study offers a novel, comprehensive comparison of clustering algorithms for Indonesian earthquake data, emphasizing a dual-metric evaluation approach. By identifying K-Means as the most effective algorithm, provides valuable insights for disaster mitigation and seismic risk analysis. |
| format | Article |
| id | doaj-art-583a8bec48a34df4be615db67bddde0c |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-583a8bec48a34df4be615db67bddde0c2025-01-13T03:30:32ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-12-018676877810.29207/resti.v8i6.55145514Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP AlgorithmsNurfidah Dwitiyanti0Siti Ayu Kumala1Shinta Dwi Handayani2Universitas Indraprasta PGRIUniversitas Indraprasta PGRIUniversitas Indraprasta PGRIIndonesia’s frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, Indonesia's frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, necessitate precise seismic zone identification to improve disaster preparedness. This research evaluates the effectiveness of five clustering algorithms—K-Medoids, K-Means, DBSCAN, Fuzzy C-Means, and K-Affinity Propagation (K-AP)—for analyzing earthquake data from January 2017 to January 2023. Using a dataset from BMKG encompassing 13,860 seismic events, each algorithm was assessed based on Silhouette Score and Cluster Purity metrics. Results indicated that K-Means provided the best balance, forming six clusters with a Silhouette Score of 0.3245 and Cluster Purity of 0.7366, making it the most suitable for seismic zone analysis. K-Medoids closely followed with a Silhouette Score of 0.3158 and Cluster Purity of 0.7190. Although DBSCAN effectively handled noise, its negative Silhouette values indicated poor clustering quality. Fuzzy C-Means and K-AP underperformed, with K-AP generating an impractically high number of clusters (196) and the lowest Silhouette Score (0.2550). This study offers a novel, comprehensive comparison of clustering algorithms for Indonesian earthquake data, emphasizing a dual-metric evaluation approach. By identifying K-Means as the most effective algorithm, provides valuable insights for disaster mitigation and seismic risk analysis.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5514cluster puritycomparative studyearthquake clusteringk-means |
| spellingShingle | Nurfidah Dwitiyanti Siti Ayu Kumala Shinta Dwi Handayani Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) cluster purity comparative study earthquake clustering k-means |
| title | Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms |
| title_full | Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms |
| title_fullStr | Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms |
| title_full_unstemmed | Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms |
| title_short | Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms |
| title_sort | comparative study of earthquake clustering in indonesia using k medoids k means dbscan fuzzy c means and k ap algorithms |
| topic | cluster purity comparative study earthquake clustering k-means |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5514 |
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