Clustering Analysis for Regional Variations in Beef Production: A Comparative Study of Hierarchical and Non-Hierarchical

Abstract. Cluster analysis is vital for identifying regional disparities in beef production and guiding targeted livestock development, particularly on Java Island. This study aimed to classify 119 regencies/cities in Java based on 2023 beef production data using both hierarchical (single linkage, average linkage, complete linkage, centroid linkage, Ward's method) and non-hierarchical (k-means, k-medoid, fuzzy c-means) clustering techniques. Performance evaluation through the Davies-Bouldin index and silhouette score revealed that the centroid linkage method delivered the most accurate results. The analysis identified three distinct clusters: Cluster 1 consisted of 90 regencies/cities with moderate beef production, Cluster 2 comprised 6 regencies/cities with high production, and Cluster 3 included 16 regencies/cities with low production. West Java records the highest production volume, while East Java has the largest cattle population. These results highlight regional differences in production capacity, and suggest the need for targeted policy interventions. To bridge potential gaps in beef availability and address regional imbalances, policies could focus on improving beef production in low-output areas and optimizing practices in high-production regions. This study also underscores the importance of considering production levels and livestock populations in regional development strategies. The findings are expected to contribute to efforts aimed at increasing domestic beef availability and reducing reliance on imports, thereby helping to meet the nutritional and protein needs of the population.