Sistem Pendukung Keputusan Penerimaan Karyawan Menggunakan Kombinasi Gray Relational Analysis dan G2M Weighting - Santika, Aisyara Zulaika Anteng; Ardiansah, Temi (Building of Informatics, Technology and Science (BITS), 2025)
An effective and efficient employee recruitment process is crucial for companies to ensure that selected candidates align with organizational needs and culture. However, the complexity of evaluation criteria often leads to subjectivity in decision-making. This study aims to develop a decision support system (DSS) for employee recruitment by integrating the Gray Relational Analysis (GRA) method and G2M Weighting. The GRA method is utilized to evaluate the relative relationships among criteria under conditions of incomplete data, while G2M Weighting provides objective weighting of criteria using a combination of gray system analysis and geometric mean. This combination of methods is designed to yield a more accurate, objective, and comprehensive ranking of candidates. The research stages include data collection for employee selection, determination of criteria weights using G2M Weighting, and candidate ranking analysis with GRA. The results of the employee recruitment selection ranking research show that for rank 1 with a final GRA value of 0.918 obtained by employee Pipit, rank 2 with a final GRA value of 0.903 obtained by employee Iswara, rank 3 with a final GRA value of 0.805 obtained by employee Riska, rank 4 with a final GRA value of 0.733 obtained by employee Andri, rank 5 with a final GRA value of 0.697 obtained by employee Ikhsan, rank 6 with a final GRA value of 0.595 obtained by employee Ani, and rank 7 with a final GRA value of 0.553 obtained by employee Estu. Which shows this approach is able to increase objectivity and efficiency in the selection process. By considering various dimensions of assessment in a balanced manner, developed system provides recommendations for the best candidates based on highest scores. The implementation this method can help companies in reducing subjective bias, improving decision quality, and minimizing the risk of recruitment errors. This research makes a significant contribution in the field of decision support systems, especially for the employee selection process, by offering innovative solutions that are adaptive to changing needs of the company.