Mamdani Fuzzy System for RPL Student Selection: A Case Study from a University in Jember

The student selection process through the Recognition of Prior Learning (RPL) pathway continues to face challenges related to objectivity and transparency, particularly in integrating qualitative and quantitative data from various assessment instruments such as interviews and problem-solving tests. This study aims to develop a decision support system based on the Mamdani-type fuzzy logic to support a fair and standardized RPL student selection process. A quantitative approach was employed through intelligent system modeling, with system implementation carried out using Microsoft Excel by modifying IF-THEN formulas to simulate fuzzy logic principles. The system incorporates two input variables interview scores and problem-solving test scores and one output variable representing admission eligibility, categorized into five levels (A-E). Testing on a sample of ten prospective students showed that the system was able to perform systematic evaluations and generate more objective and consistent admission decisions. The conclusion of this study is that the application of a fuzzy inference system can improve the quality of decision making in the selection of RPL students, as well as support the principles of competency based and fair assessment.