CLUSTERING KETIDAKCUKUPAN KONSUMSI PANGAN PER KABUPATEN/KOTA MENGGUNAKAN ALGORITMA K-MEANS DAN FUZZY C-MEANS

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Published: Apr 20, 2024
Keywords:
Food Consumption Inadequacy, K-Means, Fuzzy C-Means, Clustering, Silhouette Score
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Jonathan Suryadi

Abstract

Food consumption inadequacy is a significant problem in Indonesia, influenced by socio-economic factors and geographical conditions. This study aims to identify the distribution pattern of food consumption inadequacy per district/city using K-Means and Fuzzy C-Means algorithms. The data used comes from the Central Bureau of Statistics, covering the prevalence of inadequate food consumption throughout Indonesia. The results show that K-Means algorithm produces clustering with higher Silhouette Score for small number of clusters, while Fuzzy C-Means provides better stability on more complex clusters. These findings provide important insights for more effective data-driven food distribution policy making.

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Vol. 19 No. 1 (2024): Jurnal Komputer dan Informatika
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Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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