Clustering of MSMEs Based on Assets and Turnover Using the K-Means Algorithm - Supriatiningsih, Supriatiningsih; Nouvel, Ahmad; Ubaidillah, Ubaidillah; Joko Dwi Mulyanto (Jurnal Multidisiplin Sahombu, 2025)
Effective UMKM assistance requires business identification and grouping. Officially, UMKM in Indonesia are divided into Micro, Small, and Medium based on assets and turnover. This research aims to group UMKM in a Regency of South Sumatra by applying the K-Means Clustering algorithm using these two variables. The research stages include business understanding, data understanding, data processing, modeling, evaluation, and dissemination. From 15 test data, this study successfully applied K-Means to classify UMKM. The result was the formation of 3 clusters, consisting of 8 data (53%) in Cluster 1, 6 data (40%) in Cluster 2, and 1 data (7%) in Cluster 3. This result has been validated using RapidMiner and shows identical outcomes. This grouping can serve as a basis for stakeholders to provide more effective assistance