Application of the K-Means Algorithm for Clustering Rubber Farmers' Income in Karya Mukti Village
DOI:
https://doi.org/10.65359/sintika.2026.22.96Keywords:
Clustering, K-Means, Rubber Farmers' Income, Data MiningAbstract
The income of rubber farmers in Karya Mukti Village varies considerably among farmers, influenced by factors such as land area, rubber latex production, and plantation maintenance costs. These differences in characteristics result in varying levels of farmer income, making it necessary to classify the farmers to identify income patterns more clearly. This study aims to classify the income levels of rubber farmers using a data mining approach with the K-Means algorithm. The analysis was conducted using data from 95 rubber farmers collected through direct interviews. The variables analyzed included land area, rubber latex production, and farmers' net income. The results show that rubber farmers in Karya Mukti Village were divided into three clusters: a low-income cluster consisting of 43 farmers, a medium-income cluster consisting of 37 farmers, and a high-income cluster consisting of 15 farmers. Evaluation using the Davies–Bouldin Index resulted in a value of 0.519, indicating that the clustering results have reasonably good quality. The findings reveal a relationship between land area, production level, and the net income of rubber farmers. This study is expected to provide an analytical basis for the village government and relevant stakeholders in developing more targeted policies to improve the welfare of rubber farmers.
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