Mapping Leading Commodities of Community Forest Plantations Based on Productivity Using the K-Means Clustering Algorithm - Taufik Hidayat; Yuni Handayani; Muhammad Khozin; Tri Muji Waluyo; Dian Novitaningrum; Tresi Aprilia; Muchamad Achsin Samas (Ali Institute of Research and Publication, 2026)
Kendal Regency has significant potential in community forest plantations, which contribute to the regional economy. However, the mapping of leading commodities based on productivity has not been conducted optimally. This study aims to map leading community forest plantation commodities using the K-Means Clustering algorithm. The novelty of this study lies in the application of the K-Means Clustering algorithm by integrating land area and production volume as the basis for mapping leading commodities at the regency level. Secondary data from the Central Bureau of Statistics of Kendal Regency for the 2019–2023 period, covering seven community forest plantation commodities, were used. The research stages included data preprocessing using Min-Max normalization, clustering into three clusters using the K-Means algorithm, and cluster evaluation employing the Within-Cluster Sum of Squares (WCSS). The results show that the K-Means algorithm successfully grouped the commodities into three clusters based on their productivity characteristics. Sugarcane formed a distinct cluster as the leading commodity due to its highest productivity despite its relatively small cultivation area. These findings provide data-driven insights to support decision-making for the development of community forest plantations in Kendal Regency