Clustering Indonesian Traditional Foods by Nutritional Profiles using the K-Means Algorithm for Health Policy
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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DOI: http://dx.doi.org/10.30829/zero.v10i2.26756
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