<![CDATA[Data utilization in decision-making within Indonesia’s poultry sector remains suboptimal despite the availability of heterogeneous production data across provinces. This study aims to cluster multi-commodity poultry production to identify regional distribution patterns and support data-driven decision-making. Unlike previous studies that mainly focused on cluster exploration and visualization without systematic relevance to policy needs, this research integrates K-Means Clustering with the CRISP-DM framework to produce a more systematic and applicable analysis. The dataset comprises poultry production data from 34 provinces in Indonesia during 2012–2023, including broiler chickens, layer chickens, ducks, quails, and native chickens as the main variables. The findings show that the optimal number of clusters, determined using the Silhouette Score, is four: very high, high, medium, and low production clusters, reflecting significant interregional disparities. The clustering pattern indicates spatial inequality, with high-production regions largely concentrated on Java Island, although North Sumatra and West Sumatra outside Java also demonstrate very high production levels, while most other provinces remain in the medium-to-low production categories. These findings provide an operational regional segmentation model and establish a measurable basis for prioritizing interventions, optimizing distribution systems, and formulating data-driven policies in Indonesia’s national poultry industry.]]>
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