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All Journal INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Putra, Indra Syah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

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Customer Segmentation of E-Wallet Top-Up Users Based on RFM and K-Means Clustering Ranja, Feri; Ginting, Aser Heber; Putra, Indra Syah; Bukit, Roswitha
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.887

Abstract

The rapid growth of digital payment systems has significantly increased the use of e-wallet services, creating challenges for banks in understanding customer transaction behavior and developing targeted marketing strategies. This study aims to segment e-wallet top-up customers using the Recency, Frequency, and Monetary (RFM) model integrated with the K-Means clustering algorithm. The research follows the CRISP-DM framework, covering business understanding, data preparation, modeling, and evaluation stages. The dataset consists of 143,836 bill payment transaction records collected from a government bank in North Sumatra over a two-month period. RFM values were calculated to measure customer engagement and transaction value, followed by clustering analysis to group customers based on behavioral similarity. The results identified three distinct customer segments: Silver, Gold, and Platinum. Evaluation metrics indicate that the clustering model produced stable, meaningful segmentation, providing strategic insights to support personalized marketing initiatives and to improve customer retention and service optimization.
Application of The RFM Model and K-Means Clustering for Customer Segmentation in E-Wallet Top-Up Services Sundari, Agus; Putra, Indra Syah; Sibuea, Nuraini
INFOMATEK Vol 28 No 1 (2026): Juni 2026 (In Progress)
Publisher : Fakultas Teknik, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/infomatek.v28i1.42246

Abstract

The implementation of digital payment technology through e-wallet top-up services requires financial institutions to understand user characteristics and behavior comprehensively The objective of this study is to segment customers based on their e-wallet top-up behavior by analyzing 143,836 bill payment transaction records using the RFM (Recency, Frequency, Monetary) model combined with the K-Means clustering algorithm. The dataset contains more than one hundred thousand transaction entries, with RFM parameters representing the time since the last transaction, the frequency of top-ups, and the monetary value spent by users. The RFM scoring process is applied to quantify user activity levels before entering the clustering stage. The K-Means clustering model successfully grouped customers into three distinct segments. The first segment represents low-activity users, the second consists of moderately active customers with stable transaction behavior, while the third segment captures highly engaged users with the highest transaction frequency and value. Evaluation metrics, including a silhouette score of 0.64, a Calinski-Harabasz index of 21690.50, and a Davies-Bouldin score of 0.70, demonstrate strong clustering performance and reliable separation between groups. The findings provide valuable insights for designing service strategies, improving mobile banking system performance, and developing targeted marketing approaches tailored to each customer segment. This research highlights the potential of RFM based clustering as a decision-support tool for enhancing digital payment service optimization and customer engagement.
Co-Authors Bukit, Roswitha Ginting, Aser Heber Ranja, Feri Sibuea, Nuraini Sundari, Agus