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All Journal Journal of Degraded and Mining Lands Management Aceh International Journal of Science and Technology
Nurjanah, Riska Siti
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences

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Hotspot Distribution in West Kalimantan Using K-Means and SOM Clustering Nurjanah, Riska Siti; Iryanti, Mimin; Rusdiana, Dadi
Aceh International Journal of Science and Technology Vol 14, No 2 (2025): August 2025
Publisher : Graduate School of Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.14.2.44065

Abstract

Indonesia has quite a large forest, and some forests often experience fires. These fires typically occur due to several factors, including high solar heat, drought in peat forests, and the practice of clearing land by burning. This research focuses on West Kalimantan, one of the areas that experiences the most frequent forest fires. To achieve this, the study employs K-Means Clustering and Self-Organizing Map (SOM) algorithms, integrated with Geographic Information System (GIS) tools, to process satellite imagery from NASAs Terra and Aqua satellites. Key parameters include geographic coordinates (latitude and longitude), brightness temperature, and hotspot confidence levels. The clustering results identified two primary groups, with Cluster 2 representing the group with the highest thermal activity and fire risk. This cluster recorded a peak brightness temperature of 432.42 K and achieved a silhouette score of 0.71, indicating high clustering validity. GIS-based mapping revealed that the Sambas region had the highest concentration of hotspots, accounting for 36.01% of all detected points. These findings underscore the importance of targeted fire prevention efforts, particularly in high-risk zones with dense vegetation and frequent fire incidents.
Landslide susceptibility mapping based on K-Means and Self-Organizing Map clustering with Geographic Information System in Tasikmalaya, West Java Iryanti, Mimin; Ardi, Nanang Dwi; Nurjanah, Riska Siti
Journal of Degraded and Mining Lands Management Vol. 13 No. 1 (2026)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2026.131.9355

Abstract

Landslides are one of the most frequent natural disasters in Indonesia, primarily caused by complex topographic conditions, high rainfall intensity, and extensive land use changes. This study aimed to map landslide-susceptibility areas in Tasikmalaya Regency, West Java, using the K-Means Clustering and Self-Organizing Map (SOM) methods, visualized through a Geographic Information Systems (GIS). The data utilized include Landsat 8 satellite imagery for calculating the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) indices, elevation and slope data derived from Digital Elevation Model (DEM), and 2024 rainfall data from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). Each variable was classified into five categories based on gridcode values to facilitate spatial analysis. The clustering results revealed two main groups, with the first cluster showing higher landslide potential due to a combination of steep slopes, moderate rainfall, and a high level of urban development. This cluster recorded a Silhouette Coefficient value of 0.75, indicating a high level of landslide vulnerability. In contrast, the other cluster represented more stable terrain, with a Silhouette Coefficient of 0.72. This study is expected to serve as a reference for developing disaster risk-based spatial planning and mitigation strategies.
Co-Authors Ikrimah, Awalia Latifa Mayda Mimin Iryanti Nanang Dwi Ardi Somantri, Wendra Riyadi Wulan Safitri, Wulan