Hybrid TOPSIS–ELECTRE Decision Support System for Objective New Employee Selection: A Web-Based Implementation Study

Abstract

Employee recruitment plays a crucial role in organizational performance; however, manual selection processes often result in subjective assessments, inconsistent evaluations, and limited transparency. This study aims to develop a web-based Decision Support System (DSS) integrating the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Elimination and Choice Expressing Reality (ELECTRE) to improve the objectivity and consistency of em16selection at Jurnal1Jambi.com. The proposed hybrid multi-criteria decision-making model evaluates candidates using six criteria: education level, test score, work experience, health, age, and legal record. TOPSIS is used to generate candidate rankings, while ELECTRE validates dominance relationships among alternatives. Experimental results show that the hybrid approach consistently identifies the best candidate with a preference value of 0.6652, indicating improved ranking stability compared with conventional procedures. The developed DSS enhances decision traceability, reduces evaluation bias, and provides a practical framework for implementing hybrid MCDM techniques in recruitment systems. This study contributes to the advancement of intelligent decision support applications in the field of informatics by demonstrating the effectiveness of integrated decision-making methods in human resource management.