Implementasi Algoritma K-Means Clustering Untuk Pengelompokan Loyalitas Pelanggan Berbasis Web di UD.Majutoto Malang - Widad, Zeda Al; Sudriyanto, Sudriyanto; Malik, Kamil (Universitas Multi Data Palembang, 2024)
Performance evaluation of a company often relies on its profitability, significantly impacted by the presence of active customers. Customer segmentation into loyal and non-loyal categories using the K-Means Clustering algorithm assists in developing subsequent strategies, including tailored incentives based on customer loyalty levels. Applying this algorithm at UD. Majutoto Malang, customers were segmented based on box purchases into three clusters with random initial centers. Out of 624 data points, the segmentation resulted in 62 non-loyal customers, 463 highly loyal customers, and 99 moderately loyal customers. The clustering accuracy was evaluated using the Davies-Bouldin Index (DBI), which yielded a score of 0.01. A DBI value close to 0 indicates good clustering quality.