Redefining Academic Recruitment Using Fuzzy Tahani Logic: A Multi-Criteria Model for Calculus Teaching Assistants’ Selection

Abstract

This study aims to implement the Tahani fuzzy logic method as a decision support system for selecting candidates to become teaching assistants in a Calculus course. The selection process involves evaluating participants based on four test items covering major topics in Calculus: definite integrals, areas between curves, and others. Each participant’s score was fuzzified into three linguistic categories: poor, fair, and good. Membership functions were constructed using triangular distributions, and fuzzified scores were processed using a rule-based inference system. The final recommendation score for each participant was obtained by defuzzifying the fuzzy outputs. Participants with a final score recommendation greater than or equal to 0.5 were classified as eligible. The results show that the fuzzy logic approach offers a flexible and effective way to handle uncertainties in assessment, with 8 participants meeting the passing criteria. This method provides an objective framework for decision-making in academic selection contexts.