Employee Performance Evaluation Using ANP and TOPSIS

In the era of competitive globalization, employee performance evaluation is crucial for ensuring productivity and quality in human resources. This research addresses the challenge of subjectivity in performance evaluation by integrating the Analytical Network Process (ANP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The study identifies relevant evaluation criteria, assigns weights using ANP, and prioritizes employee performance objectively through TOPSIS. Using a Research and Development (RnD) approach, data were collected via observations, interviews, and documentation. Results demonstrate that the combination of ANP and TOPSIS significantly improves the accuracy and fairness of evaluations, reducing bias by 20% and enhancing transparency by 15% compared to traditional methods. Employees with a preference score of 1.00, such as Sumadin, Siti, and Ardianto, were deemed to have optimal performance across the criteria: Responsibility, Attendance, Service, Cleanliness, and Loyalty. The system also categorized employees with medium preference values (0.6–0.9) and low scores (<0.4), providing actionable insights for employee development. This research highlights the efficacy of technology-based evaluation systems in strategic HR decision-making, contributing to increased job satisfaction and productivity. The system developed has proven to be efficient, able to reduce bias, and increase job satisfaction and productivity.