A Decision Support Model for Scholarship Recipient Selection Based on Tsukamoto Fuzzy Logic

Muhtadi
Aksa, Muhammad
Naoval, Ahmad
Adiba, Fhatiah
Nasurllah, Asmaul Husna

Media of Computer Science
Vol. 2 No. 1 (2025): June 2025

This study proposes a decision support model for scholarship recipient selection based on the Tsukamoto fuzzy logic method to overcome the inefficiencies and subjectivity inherent in manual selection processes. The model incorporates three key criteria: Grade Point Average (GPA), parents’ income, and number of dependents. Experiments were conducted using a dataset of 25 students obtained from a public Kaggle repository. The model employs fuzzification, rule formulation, and defuzzification to compute a final decision score for each applicant. The experimental results demonstrate that the proposed model achieves an accuracy rate of 92%, indicating its effectiveness in supporting objective and efficient scholarship selection decisions.