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All Journal Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Putu Satria Dharma Wibawa
Universitas Udayana
Computer Science & IT Engineering

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Analisis Perbandingan K-Means++, Mini Batch K-Means, dan Fuzzy C-Means pada Segmentasi Pelanggan I Putu Satria Dharma Wibawa; Made Agung Raharja
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p19

Abstract

Customer segmentation is a crucial process for optimizing marketing strategies This study aims to implement and compare three clustering algorithms on customer transaction data using RFMT (Recency, Frequency, Monetary, and Tenure) features. The dataset, obtained from the UCI Machine Learning Repository, underwent several preprocessing stages, including data cleaning, feature extraction, outlier handling, and normalization. Optimal cluster numbers were determined using the elbow method and validated using silhouette score and davies-bouldin index. The results show that mini batch k-means outperforms the other algorithms with the highest silhouette score of 0.4011 and the lowest davies-bouldin index of 0.9521. K-means++ demonstrated better computation time but slightly lower clustering quality, while fuzzy c-means produced less distinct segmentation
Co-Authors Raharja, Made Agung