Pendekatan Berbasis Logika Fuzzy untuk Pemodelan Ketidakpastian dalam Evaluasi Ketercapaian Capaian Pembelajaran Lulusan (CPL) dalam Kerangka Outcome-Based Education (OBE)

Abstract

Although the Outcome-Based Education (OBE) curriculum is now a mandatory standard in higher education accreditation, precise measurement of Graduate Learning Outcomes (CPL) still faces major challenges. Current approaches generally rely on linear weighted averages, treating qualitative rubrics as rigid numerical values. As a result, these static methods often ignore uncertainty and subjectivity, especially in borderline student cases. This study proposes a CPL evaluation model based on fuzzy logic using a Fuzzy Inference System (FIS). The model processes assessment components, including formative metrics (X1), summative metrics (X2), and affective metrics (X3), to produce a more adaptive CPL score (Y). Simulations were conducted using MATLAB by applying trapezoidal and triangular membership functions, along with centroid defuzzification. Validation results from thirty student samples show that the fuzzy model demonstrates nonlinear sensitivity, with score differences ranging from -8.4 to +2.9 points compared to traditional linear methods. These findings confirm that fuzzy logic provides mathematical compensation, where consistency in daily assignments and affective aspects can mitigate anomalies in summative exam results. This model contributes practically to strengthening quality assurance through a more transparent and representative assessment system.