A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection
This addresses scholarship selection for educational institutions, but it is incremental as it applies existing decision-making methods to a specific dataset.
The study tackled the problem of selecting scholarship recipients from many applicants by applying Fuzzy Multiple Attribute Decision Making (FMADM) with TOPSIS and Weighted Product methods to data from Universitas Islam Negeri Sunan Kalijaga, and the results showed these methods could identify the most suitable candidates based on preference values.
As the education fees are becoming more expensive, more students apply for scholarships. Consequently, hundreds and even thousands of applications need to be handled by the sponsor. To solve the problems, some alternatives based on several attributes (criteria) need to be selected. In order to make a decision on such fuzzy problems, Fuzzy Multiple Attribute Decision Making (FMDAM) can be applied. In this study, Unified Modeling Language (UML) in FMADM with TOPSIS and Weighted Product (WP) methods is applied to select the candidates for academic and non-academic scholarships at Universitas Islam Negeri Sunan Kalijaga. Data used were a crisp and fuzzy data. The results show that TOPSIS and Weighted Product FMADM methods can be used to select the most suitable candidates to receive the scholarships since the preference values applied in this method can show applicants with the highest eligibility