<![CDATA[The rapid adoption of digital payment technologies, such as e-wallets and mobile banking, has significantly altered the financial habits of university students, often blurring the psychological boundaries of spending compared to conventional cash transactions. This study aims to objectively map and cluster the multidimensional consumptive behavior profiles of university students based on their payment preferences and living status. A quantitative approach was employed using a dataset of 150 respondents evaluated across 15 financial and behavioral variables. To address the complexity of the high-dimensional data, preprocessing techniques including One-Hot Encoding, Min-Max Scaling, and Principal Component Analysis (PCA) were applied prior to modeling. The study utilized a comparative clustering approach to evaluate the performance of the K-Medoids and DBSCAN algorithms. The evaluation metrics demonstrate that DBSCAN (, MinPts ) outperformed K-Medoids, achieving an optimal Silhouette Score of 0.5438, a Davies-Bouldin Index of 0.7151, and a Calinski-Harabasz Index of 101.57. The DBSCAN model successfully mapped the data into six distinct consumer clusters. The profiling results reveal a significant behavioral divide: students preferring digital payments exhibit notably higher consumptive tendencies, impulsivity, and reliance on PayLater credit facilities compared to highly conservative cash users. Furthermore, living status dictates fixed expense allocations, compounding the financial vulnerabilities of specific groups. These empirical findings provide a robust data-driven foundation for developing targeted financial literacy strategies to help students manage digital financial ecosystems responsibly Keywords: Consumptive Behavior, Clustering, DBSCAN, K-Medoids, Digital Payment.]]>
