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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Inferensi JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Journal of Research Applications in Community Services
Meirinda Fauziyah
Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Mulawarman University, Indonesia
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Jurnal Statistika Universitas Muhammadiyah Semarang

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K-Means Algorithm for Grouping Provinces in Indonesia Based on Macroeconomic and Criminality Indicators Andrea Tri Rian Dani; Fachrian Bimantoro Putra; Meirinda Fauziyah; Sifriyani Sifriyani; Suyitno Suyitno; M Fathurahman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.2.2023.12-21

Abstract

Cluster analysis is a method in multivariate analysis to group n observations into K groups (K ≤ n) based on their characteristics. One of the well-known algorithms in cluster analysis is K-Means. K-Means uses the non-hierarchical principle where at the initial initiation, it is necessary to determine the number of groups in advance. The K-Means algorithm can be applied to classify provinces in Indonesia based on macroeconomic indicators (percentage of poor people, open unemployment rate, and Gini ratio) and crime rate (Crime rate). The ultimate goal of this research is of course to get optimal grouping results. The similarity measure used is Euclidean Distance. The number of groups tested K=2,3,4,…,10 and the optimal number of groups with the highest Silhouette value was selected. Based on the results of the analysis, the optimal number of clusters is four. These four clusters have characteristics that distinguish one cluster from another.
Co-Authors Andrea Tri Rian Dani Dani, Andrea Tri Rian Fachrian Bimantoro Putra Fachrian Bimantoro Putra Hadi Koirudin Hardina Sandariria M. Fathurahman Muhammad Aldani Zen NARITA YURI ADRIANINGSIH Qonita Qurrota A'yun Sifriyani Sifriyani Sifriyani, Sifriyani Siti Mahmuda Sri Wahyuningsih Sri Wigantono Suyitno Suyitno Suyitno Suyitno Vita Ratnasari