Pemanfaatan Data Transaksi untuk Dasar membangun Strategi berdasarkan Karakteristik Pelanggan dengan Algoritma K-Means Clustering dan Model RFM - Carudin (Jurnal Teknologi Terpadu, 2021)
Every time there is a transaction process carried out by a customer, that process adds to the data collection in a database. This study uses transaction data to determine customer segmentation and build a strategy based on customer characteristics with the RFM and K-Means model approach. K-Means Clustering is an algorithm that can produce a visual cluster model with the Rapidminer application version 9.9, using the RFM attribute to represent the number of customers from each cluster. The transaction data for the last three years, 2017, 2018, and 2019 with 4,332 transactions, were then managed based on the RFM model resulting in 1898 customers. Furthermore, a cluster analysis carries out using the K-Means algorithm with 319 customers in cluster 1, 314 customers in cluster 2, 316 customers in cluster 3, 317 customers in cluster 4, 315 customers in cluster 5, and 317 customers in cluster 6. The company can use the results of this study to determine customer characteristics and as a consideration for making a new strategy.