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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika
Muhumatul Ifadah Ifadah
Universitas Muhadi Setiabudi
Computer Science & IT

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Penerapan Metode K-Means pada Pengelompokan Persebaran Kasus Diabetes Mellitus di Kecamatan Kersana Muhumatul Ifadah Ifadah; Agyztia Premana; Nur Ariesanto Ramdhan
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7257

Abstract

Diabetes Mellitus is a non-communicable disease that requires attention in public health prevention and control efforts. The distribution of cases varies across villages, requiring an analytical method to identify regional groups based on case levels. This study aims to apply the K-Means Clustering method to classify the distribution of Diabetes Mellitus cases in 13 villages in Kersana District. The data consist of 365 cases, comprising 82 male cases and 283 female cases. This study uses a descriptive quantitative approach, with the total number of Diabetes Mellitus cases as the main clustering variable, while gender data are used as supporting descriptive information. The clustering process divides the villages into three clusters: low, medium, and high. The results show that the low-level cluster consists of 9 villages with an average of 15.33 cases, the medium-level cluster consists of 2 villages with an average of 36 cases, and the high-level cluster consists of 2 villages with an average of 77.50 cases. Limbangan and Kradenan are categorized as high-level areas, Cikandang and Kersana as medium-level areas, while Cigedog, Kubangpari, Ciampel, Jagapura, Sutamaja, Kemukten, Sindangjaya, Pande, and Keramatsampang are categorized as low-level areas. The evaluation using the Silhouette Score resulted in a value of 0.690, indicating a reasonably good cluster structure. The results demonstrate that K-Means can help identify regional groups based on Diabetes Mellitus case levels and provide supporting information for prioritizing health monitoring and planning health programs.
Co-Authors Agyztia Premana Nur Ariesanto Ramdhan