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All Journal Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering)
Sri Dewi
Computer Science, Medan State of University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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K-Means and K-Medoids Algorithm Comparison for Clustering Forest Fire Location in Indonesia Ichwanul Muslim Karo Karo; Sri Dewi; Mardiana Mardiana; Fanny Ramadhani; Putri Harliana
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 10 No 1 (2023): Jurnal Ecotipe, April 2023
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v10i1.3896

Abstract

Forest fires are the most common cause of deforestation in Indonesia. This condition has a negative impact on the survival of living things. Of course, this has received special attention from various parties. One effort that can be made for prevention is to group these points into areas with the potential for fire using the clustering method. In this research, a comparative study of the clustering algorithm between K-Means and K-Medoids was conducted on hotspot location data obtained from Global Forest Watch (GFW). Besides that, important variables that affect the clustering process are also analyzed in terms of feature importance. There are nine important variables used in the clustering process, of which the Acq_time variable is the most important. The cluster quality of both algorithms is evaluated using the silhouette coefficient (SC). Both algorithms are capable of producing strong clusters. The best number of clusters is six clusters. The K-medoids algorithm is better at grouping data than K-means.
Co-Authors Fanny Ramadhani Ichwanul Muslim Karo Karo Mardiana Mardiana Putri Harliana