Clustering Pelanggan dengan K-Means Menggunakan Parameter Frekuensi Belanja dan Total Pengeluaran - Budianto, Rahman; Hasbi Firmansyah; Rizki Prasetyo Tulodo; Wahyu Asriyani (Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research, 2025)
Customer segmentation is an important strategy for understanding consumer behavior patterns. This study aims to group customers using the K-Means algorithm based on purchase frequency and total expenditure. The research process was conducted using RapidMiner as a data mining tool for data processing and analysis. The research stages include dataset collection, data preprocessing, implementation of the K-Means algorithm, and evaluation of clustering quality using the Performance (Clustering) method. The number of clusters used in this study is two clusters (k = 2). The results show that the K-Means algorithm is able to form customer groups with different characteristics. Model evaluation produces a positive Silhouette Coefficient and a relatively low Davies–Bouldin Index, indicating good internal cluster cohesion and clear separation between clusters. Therefore, the application of the K-Means method using RapidMiner can be utilized as a solution to support customer segmentation and assist decision-making in marketing strategies.