<![CDATA[Decision Support System (DSS) is one of the essential tools in assisting complex decision-making processes, including creditworthiness assessment based on customers’ financial profiles. This study aims to design a DSS capable of evaluating credit eligibility more accurately, objectively, and efficiently by applying the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. This method is used to rank customer alternatives based on their proximity to an ideal solution by considering several criteria such as income, expenses, collateral, and credit history. The data used in this research were obtained from customer financial datasets containing information related to their financial profiles. The system was tested using simulation data, and the results showed that the TOPSIS method can provide creditworthiness evaluations with a high level of accuracy while reducing time and errors compared to manual assessment methods. The final research results identified the best alternative as A3 with a score of 0.8859, indicating the most optimal credit eligibility level. These findings are expected to serve as a valuable reference for financial institutions in making credit approval decisions, improving transparency, and minimizing risks in the credit process. The implementation of the TOPSIS method has proven to be an effective approach in supporting data-driven decision-making.]]>
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