Clustering Indonesian Traditional Foods by Nutritional Profiles using the K-Means Algorithm for Health Policy

Authors

  • Irene Devi Damayanti Department of Informatics Engineering, Universitas Kristen Indonesia Toraja, 91811, Indonesia
  • Samuel Yacobus Padang Department of Informatics Engineering, Universitas Kristen Indonesia Toraja, 91811, Indonesia
  • Isak Tandi Department of Informatics Engineering, Universitas Kristen Indonesia Toraja, 91811, Indonesia
  • Melda Duma' Department of Agrotechnology, Universitas Kristen Indonesia Toraja, 91811, Indonesia

DOI:

https://doi.org/10.30829/zero.v10i2.26756

Keywords:

Clustering, Health policy, Indonesian traditional foods, K-Means algorithm, Nutritional profiles.

Abstract

Indonesia has a wide variety of traditional foods; however, systematic mapping of their nutritional composition remains limited. This study aims to cluster Indonesian traditional foods based on nutritional profiles using the K-Means algorithm to support health policy development. The analysis focuses on calories, protein, fat, and carbohydrates. A quantitative approach was applied, including data selection, normalization, and determination of optimal number of clusters using the Elbow Method. The results show that four clusters (k = 4) were obtained. Cluster 3 contains foods high in calories and protein, Cluster 2 is dominated by carbohydrates, Cluster 0 shows moderate nutritional values, and Cluster 1 represents low-energy foods. Clustering quality was evaluated using the Silhouette Coefficient (0.45) and Davies–Bouldin Index (0.94), indicating moderate and acceptable clustering performance. PCA visualization retained 88.77% of total data variance. Clusters inform policy via dietary grouping, guiding interventions; limited to macronutrients, excluding micronutrients and portion variability.

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Published

2026-07-29

Issue

Vol. 10 No. 2 (2026): Zero: Jurnal Sains Matematika dan Terapan

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Articles

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