Sistem Pendukung Keputusan Penentuan Instruktur Terbaik Dengan Kombinasi Metode GADA dan GRA Pada Tecnho Garage

Abstract

Instructors play a vital role in the success of training programs at non-formal education institutions such as Techno Garage. The selection of the best instructor has traditionally been conducted manually, making it prone to subjectivity and inconsistency in decision-making. To address this issue, this study designs a web-based decision support system that combines two multi-criteria decision-making methods: Grey Absolute Decision Analysis (GADA) and Grey Relational Analysis (GRA). The GADA method is used to assign relative weights to each criterion and prioritize alternatives based on absolute values, while the GRA method is applied to handle uncertainty and complex relationships among criteria. The evaluation criteria include attendance, length of service, participant feedback, and contributions to material development. The results demonstrate that the combination of GADA and GRA methods provides more objective and accurate recommendations in selecting the best instructor. This system enhances the efficiency, transparency, and accountability of the instructor selection process.