Enhancing Scholarship Recipient Selection: A Fuzzy Logic-Based Decision Support System Utilizing the Tsukamoto Method - Rustanto, Herian Setya (International Journal of Enterprise Modelling, 2024)
Cholarship recipient selection stands as a critical yet challenging process, often constrained by traditional methods reliant on rigid criteria that struggle to encompass the diverse talents and achievements of applicants. This research introduces a novel Decision Support System (DSS) empowered by fuzzy logic and the Tsukamoto method to revolutionize this selection process. The DSS transcends the limitations of conventional methods by accommodating imprecise data, embracing a holistic evaluation framework that considers multifaceted criteria such as academic performance, extracurricular activities, and personal attributes. Leveraging fuzzy rules and linguistic variables, the system navigates uncertainty, fostering fairness, and transparency in decision-making. Rigorously tested for efficiency, accuracy, and reliability, the DSS emerges as a transformative tool, redefining scholarship selection paradigms. This research not only presents a cutting-edge system but also sets a precedent for advanced decision support systems, marking a shift towards more inclusive, adaptable, and precise evaluation methodologies.