<![CDATA[The selection process for regional head candidates by political parties often faces challenges of subjective assessment and conflicting criteria. Conventional selection methods have proven to be biased. This study aims to develop a Hybrid Multi-Criteria Decision Making (MCDM) model integrating Entropy as an objective weighter, with WASPAS and VIKOR as ranking engines, within a Web-based Decision Support System (DSS). A quantitative approach was used through the simulation of heterogeneous candidate data. The Entropy algorithm automatically determines weights based on data dispersion, which are then injected into WASPAS and VIKOR for ranking. Test results show Financial Capability (36.49%) and Bureaucratic Experience (27.27%) as the main determinants. A divergence was found: WASPAS recommended a stable candidate (Candidate J), while VIKOR recommended a candidate with maximum resources (Candidate G). Through Mean Rank consensus, Candidate G was determined as the best recommendation. Comparative tests show the Hybrid model correlates strongly (rs=0.81) with SAW, yet its final decision is more strategic. In conclusion, this model effectively reduces subjectivity and provides a robust compromise solution in political selection.]]>
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