Integrating Method based on the Removal Effects of Criteria in Multi-Attribute Utility Theory for Employee Admissions Decision Making

Effective employee onboarding is essential for the success of an organization because it can ensure that the company acquires quality human resources that are in line with the needs and culture of the company. Careful employee recruitment based on objective evaluation is key in creating a competent team and supporting the achievement of the company's goals. Problems in employee recruitment often arise due to a lack of an objective and transparent selection process, which can lead to improper selection of candidates. One of the main challenges is the presence of errors in judgment, which reduces the diversity and quality of the team formed. The purpose of the study is to combine the principles of multi-attribute utility theory (MAUT) with method based on the removal effects of criteria (MEREC) to improve the decision-making process in employee recruitment which can improve objectivity, accuracy, and efficiency in the recruitment process, as well as reduce possible errors in the assessment of candidates. The results of the employee acceptance ranking using a combination of MEREC and MAUT were obtained by Clara Wijaya occupying the first position with the highest score of 0.7606, followed by Farah Ramadhani with a score of 0.7525. The third position was filled by Andi Santoso with a score of 0.4874. These ratings provide an overview of each individual's performance or eligibility based on a specific assessment.