Application of Fuzzy Logic for Major Determination for New Students at Nahdlatul Ulama University of Lampung

Abstract

The process of determining academic majors for new students is one of the main challenges at Nahdlatul Ulama University of Lampung (UNULA), as it involves various factors such as academic performance, interests, talents, and individual preferences. To address this issue, this study implements fuzzy logic using the Tsukamoto method as an approach to determine the most suitable major for new students. The Tsukamoto fuzzy method is chosen due to its ability to handle uncertainty and subjectivity in data through a rule-based inference approach. The system utilizes several input variables, including entrance exam scores, interest and aptitude test results, and interview outcomes, which are processed using membership functions and fuzzy rules. The system’s output is a recommended academic major, calculated through weighted average–based defuzzification. The implementation results show that the Tsukamoto fuzzy method can provide more accurate and appropriate major recommendations aligned with students’ profiles. This system is expected to improve the efficiency and objectivity of the major selection process at UNULA and support the optimal development of students’ potential