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

doi:10.55537/cosie.v5i3.1797

Authors

Taufik Hidayat Universitas Selamat Sri
Yuni Handayani Universitas Selamat Sri
Muhammad Khozin Universitas Selamat Sri
Tri Muji Waluyo Universitas Selamat Sri
Dian Novitaningrum Universitas Selamat Sri
Tresi Aprilia Universitas Selamat Sri
Muchamad Achsin Samas Universitas Selamat Sri

DOI:

https://doi.org/10.55537/cosie.v5i3.1797

Keywords:

K-Means Clustering, Leading Commodities, Community Forest Plantations

Abstract

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

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