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All Journal Journal of Mathematics and Scientific Computing With Applications
Ilka Zufria
Department of Computer Science, State Islamic University of North Sumatra, Medan, 20353, Indonesia
Mathematics

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MARKET SEGMENTATION OF STUDENT E-WALLET USERS USING K-MEANS CLUSTERING AND RANDOM FOREST Diah Putri Kartikasari; Ilka Zufria
Journal of Mathematics and Scientific Computing With Applications Vol. 7 No. 1 (2026)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v7i1.1582

Abstract

The rapid growth of e-wallet use among students creates a need for computational workflows that explore variation in survey-based behavior and perception. This study aimed to segment student e-wallet users and identify features associated with the resulting clusters. Data were collected through an online questionnaire, and 569 valid responses were analyzed using 13 behavioral and perception features. K-Means produced two exploratory clusters: High-Perception Promo-Responsive Users (86.3%) and Lower-Perception Selective Users (13.7%). Because internal indices disagreed and the Silhouette score of 0.568 indicated only moderate separation, k = 2 was retained as an interpretability-oriented judgment; Silhouette and Davies-Bouldin supported k = 2, whereas Calinski-Harabasz and the elbow pattern supported k = 3. Random Forest was applied as a post-clustering interpretability tool to examine label reproducibility and identify influential features. Ease of use, trust, promotion and incentives, security, and risk perception were dominant, but the self-reported data require cautious interpretation.
Co-Authors Diah Putri Kartikasari