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Chandrawati
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A Study of Grouping of Earthquake Damage from Magnitude Scale in Lombok Using K-Means Modeling Kertanah; Alissa Chintyana; Chandrawati; Basirun; Mutia Rosiana Nita Putri; Nasibatul Mahmudah
Kappa Journal Vol 8 No 3 (2024): Desember
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/kpj.v8i3.27563

Abstract

This study aims to group earthquake damage from its magnitude scale and visualize it on a geographical map. The magnitude of the earthquake was grouped using the K-means model. It is one of the most popular and effective clustering models in grouping data, such as earthquake data. The dataset used in this study is earthquake data for the last ten years on Lombok Island. The optimal number of clusters was used which is 2 in this case, based on the highest Silhouette score of 0.930. The highest Silhouette score shows the optimal number of clusters. The cluster on the geographical map shows most earthquakes' distribution in Northen Lombok Island with cluster 1 consisting of 145 earthquakes, while cluster 2 consists of 3 earthquakes. In addition, the earthquake's damage based on its magnitude scale, there were four different kinds of earthquake damage: slight, limited, minor, and severe damage that have occurred for the last ten years in Lombok Island. Minor and Slight damages were dominant, respectively. However, severe damage occurred in the northern part of Lombok Island due to an earthquake in 2018.
APPLICATION OF THE GUSTAFSON–KESSEL ALGORITHM FOR IDENTIFYING SPATIAL PATTERNS OF NATURAL DISASTERS IN EAST NUSA TENGGARA Nufus, Mitha Rabiyatul; Chandrawati; Widyaningrum, Erlyne Nadhilah
Jurnal Statistika dan Aplikasinya Vol. 9 No. 2 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09205

Abstract

This study examines spatial patterns of disaster vulnerability across districts and cities in East Nusa Tenggara Province, one of Indonesia’s most disaster-prone regions. Although previous studies have highlighted the province’s exposure to multiple hazards, limited attention has been given to clustering methods capable of capturing non-homogeneous and elliptical data structures. This research aims to classify regional disaster vulnerability based on the characteristics of disaster occurrences and to provide empirical support for more targeted mitigation strategies. Secondary data on floods, forest fires, hurricanes, and landslides recorded in 2023 were analyzed using the adaptive Gustafson–Kessel clustering algorithm. The optimal number of clusters was determined using the Silhouette validity index. The results identify three distinct vulnerability groups: regions highly prone to multiple types of disasters, regions predominantly affected by a single hazard, and regions with relatively low disaster risk. The resulting spatial patterns reveal clear differences in disaster intensity and complexity among regions, emphasizing the need for location-specific disaster management policies. This study contributes to disaster risk analysis by demonstrating the applicability of the Gustafson–Kessel algorithm in capturing complex spatial vulnerability patterns that are often overlooked by conventional clustering approaches.
Co-Authors Alissa Chintyana Basirun Erlyne Nadhilah Widyaningrum Kertanah Mitha Rabiyatul Nufus Mutia Rosiana Nita Putri Nasibatul Mahmudah