Decision Support System Integrating Entropy Weighting and MARCOS Ranking for Multi-Criteria Data-Driven Prioritization - Lince Tomoria Sianturi; Berto Nadeak; Asyahri Hadi Nasyuha; Moses Adeolu Agoi (Program Studi Teknik Komputer, 2026)
Purpose – This study aims to develop a data-driven decision support system that integrates entropy-based objective weighting with the MARCOS ranking method to improve multi-criteria prioritization in credit risk assessment by enhancing objectivity, consistency, and robustness of decision-making outcomes. Design/methods/approach – A hybrid MCDM framework is proposed, combining entropy weighting to determine criterion importance based on data variability and the MARCOS method to rank alternatives relative to ideal and anti-ideal solutions. The approach is evaluated using the Statlog German Credit dataset consisting of 1,000 applicants and six evaluation criteria. Performance is assessed through comparative analysis with conventional methods (TOPSIS and VIKOR), sensitivity testing under weight perturbation, and stability analysis using Spearman rank correlation. Findings - The results demonstrate that the proposed Entropy–MARCOS framework produces reliable and consistent prioritization outcomes. The model achieves a high ranking stability with a Spearman correlation of 0.91 and outperforms conventional MCDM methods in terms of ranking consistency. The findings also indicate that criteria such as age and employment duration have the highest discriminative importance, and the method remains robust under moderate variations in criterion weights. Research implications/limitations – However, the evaluation is limited to a single dataset and static criteria weights, which may affect generalizability across different domains or dynamic environments. Future research should explore adaptive weighting mechanisms and validate the model on more diverse datasets. Originality/value – This research contributes a unified hybrid framework that combines entropy-based objective weighting with the MARCOS ranking method, providing a more transparent, data-driven, and stable approach for multi-criteria decision-making, particularly in credit risk prioritization contexts.