Penerapan Algoritma K-Means dan DBSCAN Untuk Segmentasi Pelanggan pada Data Transaksi E-Commerce

Authors

  • Howard Chen STMIK Time
  • Joni Joni STMIK Time
  • Devi Devi STMIK Time

DOI:

https://doi.org/10.54259/jdmis.v4i2.7813

Keywords:

clustering, K-Means, DBSCAN, e-commerce, Customer Segmentation, Segmentasi Pelanggan

Abstract

This study aimed to perform customer segmentation on e-commerce transaction data using K-Means and Density-Based Spatial clustering of Applications with noise (DBSCAN) algorithms. Transaction data were transformed using the Recency, Frequency, and Monetary (RFM) model and normalized using the Min-max scaling method. The performance of both algorithms was evaluated based on the silhouette score, the number of generated clusters, and computational time efficiency. The experimental results indicated that the K-Means algorithm was superior and more functional for customer segmentation on this dataset, successfully forming two stable clusters with an instant processing time of 0.004 seconds. Conversely, the DBSCAN algorithm failed to perform proper segmentation by generating only a single cluster, despite producing a mathematically higher silhouette score of 0.427. Additionally, this study implemented a web-based simulation system using the Flask framework to facilitate the visualization of segmentation results. This study concluded that the K-Means algorithm was the most adaptive and recommended method to support business decision-making.

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Published

2026-08-20

How to Cite

Chen, H., Joni, J., & Devi, D. (2026). Penerapan Algoritma K-Means dan DBSCAN Untuk Segmentasi Pelanggan pada Data Transaksi E-Commerce. JDMIS: Journal of Data Mining and Information Systems, 4(2), 78–86. https://doi.org/10.54259/jdmis.v4i2.7813

Issue

Vol. 4 No. 2 (2026): August 2026

Section

Articles

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Copyright (c) 2026 Howard Chen, Joni, Devi

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