Implementation of Profile Matching Method for E-Wallet Selection Recommendations in Indonesia

Abstract

The development of e-wallet adoption in Indonesia accompanied by a diversity of features and service quality triggers user confusion in determining the most suitable application. This study develops a Decision Support System (SKS) to recommend the best e-wallet objectively. Using Profile Matching, the research compares the actual profile of each e-wallet against the ideal profile based on 5 criteria and 15 sub-criteria. Data obtained from 30 respondents. The process includes GAP mapping, weight conversion, Core Factor-Secondary Factor clustering, total value calculation per criteria, and then weighted aggregation for ranking. The final recommendation places DANA as the best alternative (3.86), followed by GoPay (3.80) and OVO (3.72). These results show that Profile Matching effectively handles multi-criteria decision-making in the consumer fintech space and provides consistent, transparent and replicable evaluation. The findings provide practical benefits for users in choosing an e-wallet as well as academic contributions in the form of structured application of decision-making methods in the context of digital payments. Further research is recommended to expand the sample, add criteria (cost, customer service, privacy), conduct a test of comparison methods, perform sensitivity tests, and integrate behavioral data to improve external validity and accuracy of recommendations.