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All Journal J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Sigit, Sigit Nugroho
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Computer Science & IT

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Forest Fire Clustering in Indonesia Using the Clustering Large Applications (CLARA) Method Arib, Muhammad Arib Alwansyah; Ridya, Ridya Destriani; Sigit, Sigit Nugroho; Nurul, Nurul Hidayati
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JKOMA.082.03

Abstract

Clustering is a process of grouping, observing or grouping classes that have similar objects. One clustering method that handles large amounts of data is clustering large applications (CLARA). This research aims to identify groups of forest fires in Indonesia using the CLARA method and to determine the characteristics of forest fires and the locations of forest fire occurrence points in Indonesia. The data used is hot spot data totaling 3,265 events, which can be obtained from the NASA LANCE–FIRM MODIS Active Fire website. The variables used to group forest fire events are latitude, longitude, brightness, frp and confidence. So by grouping 3,265 hot spot data by determining the optimum cluster using the Shilhoutte index and Dunn index values, the optimum cluster results were obtained, namely 2 clusters
Bahasa Inggris Arib, Muhammad Arib Alwansyah; Viola, Viola Oktamelisa; Sigit, Sigit Nugroho; Etis, Etis Sunandi
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JKOMA.082.04

Abstract

Clustering is a data grouping method applied to identifies groups formed by combining elements that have the same characteristics. One of the clustering methods that can be used is the K-Medoids method known as Partitioning Around Medoids (PAM). This study aims to obtain grouping and determine the characteristics of the results of grouping regencies/cities in the Sumatra Region based on the percentage of poverty using the K-medoids cluster method. The data used are poverty data per district/city totaling 154 in the Sumatra Region with the variables used being the expected length of schooling, average length of schooling, open unemployment rate, and percentage of poor population. The results obtained in this study are that districts/cities in the Sumatra Region have 2 optimum clusters as seen from the silhouette index value and davies-bouldin index value
Co-Authors Alwansyah, Muhammad Arib Etis, Etis Sunandi Nurul, Nurul Hidayati Ridya, Ridya Destriani Viola, Viola Oktamelisa