A Fuzzy Logic-Based Scoring Model for Comprehensive Physical Fitness Assessment in Senior High School

Abstract

Physical fitness assessments were implemented in educational institutions, military, or police for admission selection and generally still used a simple average method. The approach was fully compensatory, where a low score in a particular test component can be compensated by a high score in another test, even though the objectives of the tests are different and cannot substitute for each other. This condition has the potential to create hidden failure, which is a significant weakness hidden behind a composite score. This study aims to develop and validate an assessment model based on the Mamdani Fuzzy Inference System (FIS) as an alternative to conventional methods. Data were obtained from 136 candidates (61 males, 75 females aged 15–17 years) on a high school in Medan, Indonesia, with  having complete six test components: push-up, sit-up, pull-up/chinning, shuttle run, 12-minute run, and swimming. The fuzzy membership functions were designed separately by gender based on the institution's official conversion table, supported by statistical analysis that confirmed significant performance differences between genders in five out of six components. Three rule base scenarios were implemented. The results showed that the conventional method passed 56 participants (41.2%), while the non-compensatory fuzzy rule only passed 5 participants (3.7%) and confirmed a significant scale of hidden failure. The main contribution of this study is the design of membership functions and the application of FIS in the context of institutional selection/certification (not pedagogical), with a rule base that can be adjusted according to selection needs. The study concludes that the fuzzy approach, explicitly and measurably, is capable of revealing hidden weaknesses behind conventional average scores..