Analysis of User Satisfaction and Loyalty in the BRI Mobile E-Banking Application using a Data Mining Approach (K-Means Clustering and Decision Tree)

Abstract

The development of information technology has driven digital transformation in the banking sector, particularly through e-banking services that provide convenience, speed, and flexibility for users in conducting financial transactions. The BRI Mobile (BRImo) application, as a digital service of Bank Rakyat Indonesia, plays an important role in improving service quality for customers. However, the emergence of negative perceptions regarding transaction notifications, along with varying user experiences, may affect user satisfaction and loyalty levels. The main problem in this study is that the patterns of user satisfaction and loyalty, as well as the most influential factors affecting both aspects, have not been clearly identified. This study aims to analyze the level of user satisfaction and loyalty toward the BRI Mobile application and to identify the dominant factors influencing them, namely ease of use, security, trust, and system performance. The research employs a quantitative approach using data mining techniques, specifically the K-Means Clustering algorithm to group users based on satisfaction levels, and a Decision Tree to determine the most influential factors affecting user loyalty. Data were collected through questionnaires distributed to 100 active BRImo users. The results show that users can be grouped into several clusters with different satisfaction characteristics. In addition, the Decision Tree analysis reveals that system performance is the most dominant factor influencing user satisfaction and loyalty, followed by ease of use, security, and trust. Overall, the combination of K-Means Clustering and Decision Tree methods provides a more comprehensive understanding of user satisfaction and loyalty patterns, and can serve as a basis for strategic decision-making to improve the quality of the BRI Mobile application services.