Analyzing Scholarship Allocation Using Fuzzy Logic Methods

This study explores the application of Fuzzy Tsukamoto and Simple Additive Weighting (SAW) methods to enhance scholarship admissions. Implemented using Python, the system evaluates multiple criteria including GPA, semester, parental income, parental allowance, age, and certificates by assigning appropriate weight values to each variable. The Fuzzy Tsukamoto method, known for its capability to handle uncertainty, combined with the SAW method for its straightforward additive approach, is employed to streamline the selection process and recommend suitable scholarship types for students. The findings demonstrate that integrating these methods significantly improves the accuracy and efficiency of scholarship selection, providing a robust tool for decision-making in academic funding.