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All Journal UNP Journal of Statistics and Data Science
Mohammad Fajri
Universitas Tadulako
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Cluster Analysis of Indonesian Provinces Based on Health Performance Indicators Using Fuzzy C-Means nurul fiskia gamayanti; Mohammad Fajri; Hartayuni Sain; Fadjryani; Iman Setiawan
UNP Journal of Statistics and Data Science Vol. 4 No. 3 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss3/529

Abstract

In accordance with the Sustainable Development Goals (SDGs), with particular emphasis on Goal 3 which focuses on ensuring a healthy life and improving the welfare of all residents across all stages of life, ranging from infancy to older adulthood, it is closely related to looking at existing health indicators. The Indonesian region consisting of 34 provinces makes policies difficult to generalize due to the different characteristics of each region. So a cluster method is needed where regional objects will be clustered into several groups so that policies will be better implemented in a formed cluster. One clustering technique that may be employed is the Fuzzy C-Means (FCM) algorithm. FCM provides a membership value in the form of degrees ranging from 0 to 1, representing the degree of strength of the relationship of a data with each group, the analysis performed using the Fuzzy C-Means (FCM) method resulted in the formation of two clusters, which represent that cluster 1 is a cluster with indications of areas that have good health, and cluster 2 is a cluster with indications of poor, Cluster 1 comprises 12 provinces, whereas Cluster 2 includes 22 provinces.
Co-Authors Fadjryani, Fadjryani Hartayuni Sain Iman Setiawan Nurul Fiskia Gamayanti