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Indra Syah Putra
Universitas Wirahusada Medan, North Sumatera, Indonesia
Computer Science & IT

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Machine Learning-Based Customer Segmentation for Mobile Banking Services using K-Means Clustering Indra Syah Putra; Alex P Karo Karo; Feri Ranja; Roswhita Bukit; Joceline Schellenberg W
INFOKUM Vol. 14 No. 94 (2026): Infokum 2026
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v14i94.3144

Abstract

This study aims to segment customers based on 2024 m-banking transaction data at a regional bank in North Sumatra using the K-Means Clustering algorithm. The research process follows the CRISP-DM stages, including data preparation, data mapping, data cleaning, and clustering using Python. From 1,035,184 transaction data, 1,024,767 valid data were obtained, grouped into several service categories (e-wallet, internet, electricity & water, and insurance). The clustering results show different customer behavior patterns in each category, ranging from micro customers with small transactions and high frequency, regular customers with medium transactions, to premium customers with large transaction values ​​but low frequency. The results of this segmentation can help banks design more targeted marketing strategies, improve operational efficiency, and support data-based fraud detection systems.
Co-Authors Alex P Karo Karo Feri Ranja Joceline Schellenberg W Roswhita Bukit