Pengelompokan Desa di Jawa Barat Berdasarkan Indeks Desa Membangun (IDM) Menggunakan ALgoritma Clustering Large Application (CLARA)

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.

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Publication Details

Journal
Jambura Journal of Probability and Statistics
Published
2025-05-31
DOI
https://doi.org/10.37905/jjps.v6i1.27450
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

Pengelompokan Desa di Jawa Barat Berdasarkan Indeks Desa Membangun (IDM) Menggunakan ALgoritma Clustering Large Application (CLARA)

Reny Rian Marliana, Muhammad Rifqi Hendriawan
Jambura Journal of Probability and Statistics
Data Mining and Machine Learning Applications
article

Pengelompokan Desa di Jawa Barat Berdasarkan Indeks Desa Membangun (IDM) Menggunakan ALgoritma Clustering Large Application (CLARA)

Reny Rian Marliana, Muhammad Rifqi Hendriawan
article en

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.

Jambura Journal of Probability and StatisticsVol. 6(1)
Openalex Percentile: Top 77%
Data Mining and Machine Learning Applications
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Pengelompokan Desa di Jawa Barat Berdasarkan Indeks Desa Membangun (IDM) Menggunakan ALgoritma Clustering Large Application (CLARA) — Reny Rian Marliana, Muhammad Rifqi Hendriawan · Jambura Journal of Probability and Statistics (2025) | TGRS Research Map | TGRS