Application Of K-Means Clustering In Grouping Customer Preferences For K-Pop Albums And Merchandise

Abstract

The increasing popularity of K-Pop in Indonesia is particularly in the purchase of physical products. THJMINE Store faces challenges in inventory management and promotional strategies due to the lack of product grouping for albums and merchandise. This study applies the K-Means Clustering algorithm to 110 sales transaction data from July 2022 to January 2025. The method used in this study is the CRISP-DM approach, which consists of the following stages: business understanding, data understanding, data preparation, modeling, and evaluation discussion. The result of the study shows that the K-Means algorithm successfully formed three clusters with customer classification: loyal customers (cluster 0), general customers (cluster 1), and premium or collector customers (cluster 2). The model evaluation results in a DBI score of 0.6342, indicating good cluster quality. These clustering results can help THJMINE Store understand customer segmentation, develop more targeted marketing strategies, and improve inventory management efficiency