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All Journal JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Hendriawan, Muhammad Rifqi
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Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Social Sciences

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Pengelompokan Desa di Jawa Barat Berdasarkan Indeks Desa Membangun (IDM) Menggunakan ALgoritma Clustering Large Application (CLARA) Hendriawan, Muhammad Rifqi; Marliana, Reny Rian
Jambura Journal of Probability and Statistics Vol 6, No 1 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i1.27450

Abstract

The Village Development Index (IDM) is an important instrument for measuring village planning and development, supporting the government in dealing with underdeveloped villages and advanced villages. IDM assesses development through three indexes, namely the Economic Resilience Index (IKE), Social Resilience Index (IKS), and Environmental Resilience Index (IKL). The aim of the study is to establish clusters of villages in West Java Province using the Clustering Large Application (CLARA) algorithm. This clustering was used to facilitate the identification of underdeveloped villages. The CLARA algorithm, which is a development of K-Medoids, is used for non-hierarchical clustering analysis with medoids as the cluster center. This study used data of IKE, IKS, and IKL from villages in West Java in 2023. The results produce three clusters, namely independent villages (cluster 1), developed villages (cluster 2), and developing villages (cluster 3). Based on the Ministry of Village, there are 1856 villages in cluster 1, 2494 villages in cluster 2, and 961 villages in cluster 3. Meanwhile, based on the CLARA algorithm, there are 1644 villages in cluster 1, 1573 villages in cluster 2, and 2094 villages in cluster 3. The global silhouette coefficient (SC) value in the CLARA algorithm shows an SC value of 0,3614. This indicates that the grouping criteria are included in the weak category, because they are between the values of 0,26-0,50.  
Co-Authors Reny Rian Marliana