The Integrated TFGBM-MEREC to Determine Criteria Weight for Selecting Excellent Student

• Published in: 2023 International Conference on University Teaching and Learning, InCULT 2023
• Main Author: Bin Hasnan Q.H.; Rodzi Z.B.M.; Binti Kamis N.H.; Mohmad S.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190687947&doi=10.1109%2fInCULT59088.2023.10482576&partnerID=40&md5=6070680105ef336f7f3be4426b6b8d3e
• DOI: 10.1109/InCULT59088.2023.10482576

The aim of this research is to determine criteria weight for selecting excellent student by using the Triangular Fuzzy Geometric Bonferroni Mean (TFGBM) and the Triangular Fuzzy MEREC (TFMEREC) techniques. The Triangular Geometric Bonferroni Mean (TFGBM) method is a relatively recent approach to decision-making that increases the accuracy of outcomes by combining triangular fuzzy numbers and the Geometric Bonferroni mean operator. The research suggests a novel strategy for decision-making that makes use of the TFMEREC approaches for addressing issues involving a few different criteria. This approach offers the potential to increase both the accuracy and effectiveness of decision-making processes in a variety of different domains. The data gathered from four different decision-makers will be utilized in the analysis to determine how effective the suggested method is. The findings of this study might make a significant contribution to the formulation of an approach that is more successful. © 2023 IEEE.