Penerapan K-Means Clustering untuk Segmentasi Pelanggan - Fathoni Dwi Atmoko (Asosiasi Riset Teknik Elektro dan Informatika Indonesia, 2025)
Public transportation, with Transjakarta as its main pillar, requires a deep understanding of customer behavior to improve service quality and maintain loyalty. This study aims to segment Transjakarta customers using data mining techniques, specifically the K-Means Clustering algorithm, based on the RFM (Recency, Frequency, Monetary/Value) behavioral model. 37,900 rows of raw transaction data were processed into a clean database, resulting in 1,917 unique customers for analysis. The RFM metrics were then normalized using Min-Max Scaler. The optimal number of clusters was evaluated using the Elbow Curve and Silhouette Score Methods, which led to the determination of k = 4 clusters. The segmentation results identified four customer groups requiring specific strategies: Cluster 3 (Champions) with high R, F, and V (requiring rewards and retention); Cluster 0 (Active, Low Value) with high R and F but low V (requiring upsells and cross-sells); Cluster 1 (Potential/At-Risk); and Cluster 2 (Dormant/Lost). Preliminary analysis (EDA) showed that nearly half of customers (49.3%) used Bank DKI cards, dominated by the productive age group (25–45 years old), with the Rusun Kapuk Muara–Penjaringan route being the busiest. The main managerial recommendation is to strengthen the partnership with Bank DKI and optimize services in this busy corridor.