Evaluation of Recruitment Candidates Based on Data Objectivity Using LOGSTA and CORASO Integration

Abstract

The employee selection process often faces problems such as evaluator subjectivity, inconsistency between criteria, and decision biases, which affect the stability of candidate ranking results. This study aims to develop a decision support system for recruitment candidate evaluation that emphasizes data objectivity and consistency in assessment results. The proposed approach integrates the LOGSTA method as an objective-criteria weighting technique based on logarithmic transformation and CORASO as an alternative ranking method based on ideal–compromise solutions. LOGSTA is used to determine criteria weights objectively based on data dispersion and information content, while CORASO is utilized to comprehensively evaluate and rank candidates. The research results show that the proposed method is capable of producing a stable and transparent ranking of candidates, as well as reducing subjective bias in the selection process. Based on the final CORASO scores, candidate Gina ranked first with a score of 0.5055, followed by Nugroho in second place with a score of 0.4039, and Saputra in third place with a score of 0.3024. Scenario analysis of changes in criteria weights also indicates that the rankings of the top candidates are relatively consistent, reaffirming the reliability of the proposed approach in supporting fair and data-driven recruitment decision-making.