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All Journal Journal of Information System Exploration and Research
Tyas Arum
Department of Computer Science, Universitas Negeri Semarang, Indonesia

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Comparative Analysis of Data Normalization Effects on RFMBased Customer Segmentation Using K-Means and DBSCAN Nabilah Zahra; Tyas Arum; Zerafica Patriawan; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim; Yusuf Enril Fathurrohman
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.86

Abstract

Customer segmentation is widely used to analyze customer transaction patterns and support effective business strategies. However, previous studies have reported inconsistent findings regarding the impact of data normalization on clustering quality across different datasets and algorithms. This study investigates the effect of data normalization on RFM-based customer segmentation using K-Means and DBSCAN. Two transaction datasets, Online Retail II and TransJakarta, were analyzed under three preprocessing scenarios: no normalization, Min-Max normalization, and Z-Score normalization. Clustering performance was evaluated using the Silhouette Score and Davies–Bouldin Index (DBI). For the Online Retail II dataset, K-Means achieved the best performance without normalization (Silhouette Score = 0.9845), while DBSCAN produced valid clusters only after Z-Score normalization. For the TransJakarta dataset, both algorithms performed best without normalization, whereas DBSCAN identified up to 20 clusters and noise points. These findings highlight that the effectiveness of normalization depends on dataset characteristics and the clustering algorithm used.
Co-Authors Dwika Ananda Agustina Pertiwi Much Aziz Muslim Nabilah Zahra Yusuf Enril Fathurrohman Zerafica Patriawan