Fuzzy Logic in Education: Profile of Students’ Readiness to Prepare for Test-Based National Selection in Study Centers

Test-Based National Selection demands students' readiness not only in material mastery, but also in critical thinking skills and high-level problem solving. Tutoring institutions have become a popular choice to improve students' readiness to face the selection, but evaluating students' readiness objectively and adaptively is still a challenge. This research develops a decision support system model based on Mamdani type fuzzy inference system to evaluate students' readiness for Test-Based National Selection. Two main indicators are used as linguistic input variables, namely study frequency and try out results. The modeling process is carried out qualitatively with the stages of fuzzification, IF-THEN rule base formulation, Mamdani inference, and defuzzification using the centroid method. Data is processed with the help of Microsoft Excel as a fuzzy logic processing tool. The results of the implementation on 30 students showed that the system was able to classify the level of readiness into three categories: not ready, moderately ready, and ready, with high precision and flexibility to data uncertainty. The findings suggest that fuzzy models can be used as adaptive and contextualized evaluation tools in tutoring environments, and support data-driven instructional decision-making.