Recommendation System for Thesis Examiner Selection using Intuitionistic Fuzzy TOPSIS method for Effective Multicriteria Decision-Making

This research looks into the recommendation system for selecting undergraduate thesis examiners in the informatics department of a vocational education institution. Throughout this investigation, the most appropriate examiners based on the predetermined criteria have been selected. This selection was made possible by combining the Intuitionistic Fuzzy (IF) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. Four lecturers were chosen as examiner candidates for the purposes of this study. The seven criteria (functional position, competencies, research group, publications, supervision experiences, education qualifications, and the period of employment) have been used by a group of decision-makers consists of three academicians to evaluate the alternatives. As an outcome of this research, the best examiners for this group have been identified. TOPSIS method, a popular algorithm for solving Multi-Criteria Decision-Making (MCDM) problems that support group decisions in intuitionistic fuzzy environments, was used as the study's methodology. At the end of this study showed that out of 4 alternative examiners, the 2nd and 3rd alternatives were the most suitable examiners compared to the remaining 2 examiners.