Assessing Microcredit Eligibility Using LOGSTA Weighting and Comprehensive Distance-Based Ranking: A Multi-Criteria Decision-Making Approach

This study aims to determine microcredit eligibility objectively and systematically through the integration of the logarithmic normalization and standard deviation (LOGSTA) weighting method with the comprehensive distance-based ranking method (COBRA) within a Decision Support System framework. LOGSTA is used to generate criterion weights objectively based on data variations, while COBRA is applied to rank alternatives by considering their comprehensive distance from the ideal conditions. The study involves several microcredit eligibility criteria and a number of prospective credit recipients analyzed quantitatively. The results indicate that the LOGSTA–COBRA integration can produce clear and measurable eligibility rankings, with Candidate A8 ranking first with a final score of 0.0345, followed by Candidate A2 with a score of 0.0319 and Candidate A7 with a score of 0.0280. Sensitivity analysis through various scenarios of changes in criteria weights shows that the main ranking structure is relatively stable, indicating the robustness of the method against variations in criteria importance. Furthermore, comparisons with several popular MCDM methods such as SAW, TOPSIS, MOORA, GRA, MAUT, and WASPAS using fixed LOGSTA weights show that the proposed method provides competitive and consistent results. These findings affirm that the LOGSTA–COBRA approach can be a reliable alternative to support more objective, transparent, and accountable microcredit eligibility decision-making.