Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering
Sulawesi is a region in Indonesia known for its significant seismic activity, and its history of impactful earthquakes makes it an area of crucial importance for in-depth analysis. This study analyses earthquake occurrence data in the Sulawesi region from 2019 to 2023 using clustering methods with t...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-08-01
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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/5819 |
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| author | Ody Octora Wijaya Rushendra |
| author_facet | Ody Octora Wijaya Rushendra |
| author_sort | Ody Octora Wijaya |
| collection | DOAJ |
| description | Sulawesi is a region in Indonesia known for its significant seismic activity, and its history of impactful earthquakes makes it an area of crucial importance for in-depth analysis. This study analyses earthquake occurrence data in the Sulawesi region from 2019 to 2023 using clustering methods with the DBSCAN algorithm. The utilization of the DBSCAN algorithm was chosen for its ability to cluster data based on spatial density, well-suited for analyzing the spatial patterns of earthquakes. DBSCAN is known for its effectiveness in identifying spatial clusters, especially in handling data with undefined density patterns. The primary aim of this research is to identify spatial earthquake occurrence patterns, classify regions with similar earthquake occurrence rates, describe the characteristics of the resulting spatial clusters, and identify seismic gap areas. The results of analysis and clustering using the DBSCAN algorithm have identified clusters with earthquake depth characteristics, which are expected to make a significant contribution to mapping and understanding earthquake vulnerability and distribution in this region. These findings can aid in more effective disaster mitigation planning, support sustainable development efforts, and enhance earthquake preparedness and response in Sulawesi. This study contributes to a better understanding of earthquake patterns and potential seismic gaps in Sulawesi, which is crucial for developing improved risk mitigation strategies and supporting sustainable development policies. |
| format | Article |
| id | doaj-art-6c8eacce667f4014963ee081c837b8bc |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-6c8eacce667f4014963ee081c837b8bc2025-01-13T03:33:02ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-08-018445446510.29207/resti.v8i4.58195819Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN ClusteringOdy Octora Wijaya0Rushendra1Universitas Mercu BuanaUniversitas Mercu BuanaSulawesi is a region in Indonesia known for its significant seismic activity, and its history of impactful earthquakes makes it an area of crucial importance for in-depth analysis. This study analyses earthquake occurrence data in the Sulawesi region from 2019 to 2023 using clustering methods with the DBSCAN algorithm. The utilization of the DBSCAN algorithm was chosen for its ability to cluster data based on spatial density, well-suited for analyzing the spatial patterns of earthquakes. DBSCAN is known for its effectiveness in identifying spatial clusters, especially in handling data with undefined density patterns. The primary aim of this research is to identify spatial earthquake occurrence patterns, classify regions with similar earthquake occurrence rates, describe the characteristics of the resulting spatial clusters, and identify seismic gap areas. The results of analysis and clustering using the DBSCAN algorithm have identified clusters with earthquake depth characteristics, which are expected to make a significant contribution to mapping and understanding earthquake vulnerability and distribution in this region. These findings can aid in more effective disaster mitigation planning, support sustainable development efforts, and enhance earthquake preparedness and response in Sulawesi. This study contributes to a better understanding of earthquake patterns and potential seismic gaps in Sulawesi, which is crucial for developing improved risk mitigation strategies and supporting sustainable development policies.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5819clusteringdbscanearthquakesulawesiseismic gap |
| spellingShingle | Ody Octora Wijaya Rushendra Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) clustering dbscan earthquake sulawesi seismic gap |
| title | Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering |
| title_full | Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering |
| title_fullStr | Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering |
| title_full_unstemmed | Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering |
| title_short | Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering |
| title_sort | analysis of sulawesi earthquake data from 2019 to 2023 using dbscan clustering |
| topic | clustering dbscan earthquake sulawesi seismic gap |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5819 |
| work_keys_str_mv | AT odyoctorawijaya analysisofsulawesiearthquakedatafrom2019to2023usingdbscanclustering AT rushendra analysisofsulawesiearthquakedatafrom2019to2023usingdbscanclustering |