Clustering Analysis for Green Economy and Citizens-Based Social Forestry Business Development Model
Abstract
This study aims to prove that clustering analysis can optimize the development model of social forestry businesses based on green economy and citizens. Clustering analysis can use machine learning methods. Some of these methods are K-Means and K-Medoids. First, the research data was obtained from the assessment results of forest edge residents. Residents assessed 13 green economy variables. The social forestry business development model based on green economy and citizens requires labeled data. Therefore, this study compares the performance of K-Means and K-Medoids to cluster the assessment data of forest edge residents. To determine its performance, this study uses three variations of k values, namely K = 4, K = 8, and K = 12. Performance testing uses the Davies Bouldin Index (DBI) method and computation time. Based on Davies Bouldin test, K-Means method is better than K-Medoids at K = 4, but K-Medoids method is better than K-Means at K = 8 and K = 12. Based on computation time test, K-Means method is better than K-Medoids. Based on this test, K-Means method is more suitable for big data and fast computing time.
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