Clustering of Data on Vegetable Crop Production in the City of Bandung Using the K-Means Algorithm

Ifano Rangga Saputra, Cahyono Budy Santoso

Abstract


Vegetable farming supports urban food security, and Bandung City is one of West Java's main horticultural centers. However, vegetable production remains unevenly distributed across its sub-districts. This study analyzes production patterns from 2018–2023 using the K-Means Clustering algorithm. The dataset includes 12 major commodities, and the analysis involves data preprocessing, determining the optimal number of clusters using the Elbow Method and Silhouette Score, applying K-Means, and visualizing results through heatmaps and PCA. The findings reveal three clusters: Cluster 0 dominated by potatoes and the "others" category; Cluster 1 dominated by kale; and Cluster 2 dominated by shallots and petsai. These patterns indicate concentrated and specialized production across specific sub-districts. The study concludes that K-Means effectively identifies multi-commodity production similarities and provides strategic insight for Business Intelligence applications in agricultural planning and policy development.


Keywords


K-Means; Clustering; Vegetable Production; Business Intelligence; Data Mining; Agricultural Analytics

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References


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DOI: https://doi.org/10.31764/justek.v8i4.35555

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