The development of digital payments in Indonesia has increased the complexity of selecting an e-wallet that aligns with user preferences. This study proposes a hybrid DSS integrating AHP and Profile Matching, enhanced by a proportional transformation of AHP weights into ideal values. Unlike conventional approaches that subjectively determine ideal values, this method ensures consistency between criteria weighting and suitability evaluation, thereby reducing bias and improving ranking stability. Data from 100 students across four universities indicate that security dominates (46%), followed by convenience & access (25%), and features & cost (29%), indicating that risk reduction and trust are key adoption factors, in line with technology acceptance theory. OVO achieved the highest score. The hybrid framework reduces subjective bias in ideal-value assignment and improves ranking stability compared to standalone AHP or Profile Matching applications. These findings provide methodological contributions and practical implications for fintech providers.
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