PENERAPAN METODE K-MEANS CLUSTERING DAN SUPPORT VECTOR MACHINE (SVM) BERBASIS MODEL RFM UNTUK KLASIFIKASI TIER PELANGGAN

Abstract

Suboptimal management of large-scale transaction data can lead to marketing inefficiencies, particularly in determining promotional strategies that do not align with customer characteristics. This study aims to map the customer loyalty of CV Ekasa's client partners, by segmenting its customers using an integrated Recency, Frequency, Monetary (RFM) model, K-Means Clustering, and Support Vector Machine (SVM) classification. The dataset comprises 287,512 raw point-of-sale transaction records collected between October 2022 and September 2025, which after preprocessing yielded 341 valid customers for RFM modeling. Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, RFM features were log-transformed and standardized before clustering. Silhouette Score evaluation across k = 1–10 identified two customer segments (k = 2, Silhouette Score = 0.482) as optimal, labeled Passive Tier and Active Tier. These cluster labels were then used as classification targets for a linear-kernel SVM, evaluated under two data-splitting scenarios (80:20 and 70:30). The model achieved 97.10% accuracy with the 80:20 split and 98.06% with the 70:30 split, with precision, recall, and F1-scores above 0.97 for both tiers in both scenarios. These findings indicate that the integrated RFM–K-Means–SVM pipeline classifies customer loyalty tiers reliably and stably. The resulting model was deployed as an interactive Streamlit dashboard, giving CV Ekasa's client partner a practical, data-driven basis for designing more targeted and efficient marketing and retention strategies.