Implementasi Algoritma Clustering K-Means untuk Segmentasi Pelanggan di E-Commerce

Mado, Priscianus Mikael Kia
Hendry, Hendry

Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Vol. 6 No. 3 (2025): September

In the increasingly advanced digital era, competition in the e-commerce world requires companies to understand customer behavior in depth in order to maintain loyalty and increase sales. This study aims to segment e-commerce customers by applying the K-means clustering algorithm using RFM (Recency, Frequency, Monetary) analysis. Customer transaction data is processed through pre-processing stages such as data cleaning and normalization, then the K-means algorithm is applied to group customers into homogeneous segments based on their purchasing behavior characteristics. Optimal grouping is obtained using the Silhouette Score evaluation metric, resulting in three main customer segments. The results of this segmentation can help companies design more effective and focused marketing strategies according to the needs of each customer segment.