Customer Segmentation Based on Recency, Frequency, Monetary Analysis Using K-Means Algorithms in Apple Ecosystem - Edwin Setiawan; Bayu Surarso; Dinar Mutiara Kusumo Nugraheni (Jurnal Penelitian Pendidikan IPA, 2025)
One of the companies in Semarang engaged in gadget sales services has an Apple Ecosystem information system for selling products from an exclusive brand, Apple. Inside there are sales transactions and also service devices iPad, Macbook Air, Macbook Pro, AirPods, Mac, and Apple Accsessories. This research uses purchase transaction data from Apple Ecosystem customers for the period 2023. The use of RFM (Recency, Frequency, Monetary) analysis helps in determining the attributes used for customer segmentation. To determine the optimal number of clusters from the RFM dataset, the Elbow method is applied. The dataset generated from RFM is grouped using the K-Means algorithm, the quality of the algorithm will be compared in cluster formation using the Silhouette Coefficient method. All procedures will be loaded into the Customer Segmentation App (RFM Clustering) web application. Customer segmentation from RFM datasets that have been clustered produces 3 optimal clusters, namely Cluster 2 is High Spenders with 326 customers, Cluster 0 is VIP Customers, Cluster 1 is Frequent Buyers. Cluster validation of k-means using the silhouette coefficient produces a value of 0.3524.