Clustering of productivity in the food, plantation, and farm sectors in Mamasa Regency using K-Means clustering analysis - Muhammad Zulfadhli; Kesumaning Dyah Larasati; Rizky Fitria Ramadhanni; Raynaldi Anggiat Samuel Siahaan; Mochamad Fatih Romadlon (Bulletin of Applied Mathematics and Mathematics Education, 2026)
The productivity of the food, plantation, and farm sectors is the main driver of the economy in Mamasa Regency. However, there are significant disparities between subdistricts, requiring targeted development strategies. This study aims to group 17 subdistricts based on the productivity characteristics of the three main sectors using the K-Means Clustering method. The secondary data analyzed includes productivity, production, planted area, and the number of farmers/ranchers for each sector. The research stages include descriptive analysis, identification of leading commodities, and classification of subdistricts based on the productivity of each sector. The analysis results divide the subdistricts into three main clusters with different characteristics. Cluster 1 (High Productivity) is dominated by leading subdistricts, such as Pana for cattle farming, Nosu for Arabica coffee, and Tabulahan for patchouli. Cluster 2 (Medium Productivity) includes subdistricts with balanced performance in several commodities, while Cluster 3 (Low Productivity) consists of subdistricts that still face challenges in land optimization, production, and cultivation efficiency. The implication of this study is cluster-based policy recommendations. Local governments are advised to implement specific strategies, such as developing cattle breeding centers in Pana, processing Arabica coffee in Nosu, and patchouli industry in Tabulahan. For low-productivity clusters, interventions are directed at improving infrastructure, access to inputs, and technological assistance. With this evidence-based strategy, local potential can be optimized, regional disparities reduced, and economic growth in Mamasa Regency can be more inclusive and sustainable.