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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic
Lukman Hakim
Universitas Mercu Buana
Computer Science & IT Decision Sciences, Operations Research & Management

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Rice Distribution Clustering for Decision Support in Jakarta Suhendra Suhendra; Lukman Hakim; Fandi Ali Mustika
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12245

Abstract

Rice distribution stability plays a strategic role in ensuring urban food security, particularly in metropolitan regions such as Jakarta where supply-demand dynamics are highly complex. This study aims to develop an integrated clustering-based decision support framework to classify regional rice distribution conditions and enhance adaptive allocation strategies. Monthly rice availability and demand data from 2021–2023 across seven administrative regions in Jakarta were analyzed using the K-Means clustering algorithm. Optimal cluster determination employed the Elbow and Silhouette methods. Cluster validation was conducted using Silhouette, Davies–Bouldin, and Calinski–Harabasz indices. A comparative analysis with Hierarchical Clustering (Ward linkage) was also performed. The clustering results were integrated into a Business Intelligence dashboard. Three optimal clusters were identified, representing high-surplus, moderate-surplus, and deficit conditions. K-Means demonstrated superior cluster compactness and separation quality compared to Hierarchical Clustering, with a Silhouette score of 0.62 and a Davies–Bouldin index of 0.41. The proposed framework improves operational transparency and supports evidence-based redistribution policies. This approach also contributes to strengthening adaptive food security management in metropolitan areas.
Co-Authors Mustika, Fandi Ali Suhendra Suhendra