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All Journal RAGAM: Journal of Statistics and Its Application
Muhammad Rizki Shofari
Universitas Lambug Mangkurat
Humanities Computer Science & IT Economics, Econometrics & Finance Mathematics Public Health

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QUICK ROBUST CLUSTERING USING LINKS (QROCK) UNTUK PENGELOMPOKAN DESA KABUPATEN BANJAR Muhammad Rizki Shofari; Oni Soesanto
RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v3i1.12805

Abstract

AbstractAccurate village profile planning needs to be done with mapping based on its characteristics. Village grouping based on the characteristics of village facilities and potential or its characteristics based on the Building Village Index indicator can help determine priorities in village development. In this study, mixed data was used, with numerical data grouping using Hierarchical Agglomerative Nesting (AGNES) algorithm and categorical data with Quick Robust Clustering Using Links (QROCK). The resulting clusters are then combined using the QROCK Ensemble algorithm (algCEBMDC). The data is sourced from the 2021 Village Potential Data Collection (PODES) by the Central Statistics Agency in 277 villages in Banjar Regency, including 18 numerical variables and 29 categorical variables. The results of the study obtained  optimal clusters based on the ratio of within-group standard deviation (SW) to between-group standard deviation (SB)  resulting in a ratio of  4.82.10-9 with a threshold of 0.4 to 0.9 resulting in 6  clusters. The  best cluster results are cluster 4 (4 villages) and cluster  3 (14  villages), then cluster 2 (villages) and cluster 1 (186  villages), and clusters that need development priorities are cluser 5 (2 villages) and cluster 6 (1 village) which are outliers based on processing results. Keywords:   Village Grouping,  Cluster, AGNES Algorithm, Quick Robust Clustering Using Links (QROCK), AlgCEBMDC
Co-Authors Soesanto, Oni