Integration of LOPCOW and ARAS Methods for Selecting the Best Employees in the Finance Division

This research is motivated by problems in the performance evaluation process of employees, which often still takes place subjectively and lacks a systematic calculation basis. This can lead to unfair assessments and the potential for incorrect decisions. To address these issues, this study employs a combination of the LOPCOW and ARAS methods as an approach in a multi-criteria decision support system. The combination of the LOPCOW and ARAS methods is necessary because they complement each other in producing a more objective and accurate decision-making process. LOPCOW plays a role in determining the criteria weights objectively based on the logarithmic variation of the data, and ARAS is used to evaluate and rank alternatives based on their proximity to the ideal condition. The LOPCOW method is used to objectively determine the weights of criteria based on relative importance, while the ARAS method is used to calculate the relative utility values of each alternative, thereby producing clear and measurable final rankings. The research results indicate that out of the six employee alternatives evaluated, Employee A2 ranked first with a score of 0.9879, followed by Employee A6 with a score of 0.982. These findings prove that the integration of the LOPCOW and ARAS methods can provide objective, accurate, and transparent results in ranking alternatives. Therefore, this approach can be relied upon as a solution to address issues of subjectivity and improve the quality of decision-making, particularly in a systematic and accountable evaluation of employee performance.