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All Journal Science Technology and Management Journal (STMJ)
Sudradjat Budi Darminto, Gafgarion
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering

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Journal : Science Technology and Management Journal (STMJ)

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Analysis of Fashion Business Customer Segmentation Based on Age Using the X-Means Algorithm Approach Sudradjat Budi Darminto, Gafgarion; Siska Narulita
Science Technology and Management Journal Vol. 6 No. 1 (2026): Januari 2026
Publisher : Fakultas Sains dan Teknologi, Universitas Nasional Karangturi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53416/stmj.v6i1.489

Abstract

The rapid development of the fashion industry in Indonesia requires business practitioners to understand customer behavior more deeply and efficiently in order to formulate targeted marketing strategies. One approach that can be used to identify customer behavior patterns is customer segmentation. This study aims to perform customer segmentation in the fashion business based on age using the X-Means algorithm. The data used were obtained from a sales transaction dataset consisting of customer, order, product, and sales information. The variables analyzed include customer age and total expenditure. To evaluate the performance of the method, the X-Means algorithm was compared with K-Means and K-Medoids. Algorithm performance was measured using the Davies–Bouldin Index (DBI). The experimental results show that X-Means produced the best performance with the lowest DBI value of 0.404 at k = 5 clusters, indicating a more representative grouping of differences in purchasing behavior based on age. The segmentation results can be utilized by fashion business practitioners to design more appropriate promotional strategies and product recommendations tailored to the characteristics of each age group. Furthermore, this study recommends the use of the Deep Embedded Clustering (DEC) approach in future research when large-scale datasets (more than 1,000 customer records) are available, as deep learning-based methods are more capable of capturing non-linear patterns and improving cluster representation quality.
Co-Authors Siska Narulita