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Julianti, Syelvia
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Computer Science & IT

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Algoritma K-Means untuk Clustering Provinsi di Indonesia Berdasarkan Kasus Stunting Julianti, Syelvia; Widiartha, I Made
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p16

Abstract

Stunting is a nutritional issue that poses a global challenge, especially in developing countries like Indonesia. According to UNICEF, Indonesia ranks among the top five countries with the highest stunting prevalence. To address this issue, clustering provinces in Indonesia each year can help ensure equitable food distribution and other resources. This can be done using the K-Means clustering algorithm, with the optimal number of clusters determined by the elbow method and evaluated using the silhouette coefficient and Davies-Bouldin index. The optimal number of clusters was found to be 3, with a silhouette coefficient of 0.50 and a Davies-Bouldin index of 0.70. In 2020, there were 15 provinces in cluster 1, 6 provinces in cluster 2, and 17 provinces in cluster 3. In 2021, 15 provinces were in cluster 1, 17 in cluster 2, and 6 in cluster 3. In 2022, there were 17 provinces in cluster 1, 14 in cluster 2, and 7 in cluster 3. In 2023, 5 provinces were in cluster 1, 14 in cluster 2, and 19 in cluster 3. By 2024, there were 18 provinces in cluster 1, 17 in cluster 2, and 3 in cluster 3. Keywords: Stunting, K-Means, Elbow Method, Silhouette Coefficient, Devies Bouldin Index
Co-Authors I Made Widiartha