Assessing Physical Fitness of Elementary School Students: A Case Study of SD Negeri 002 Lubuk Baja, Batam City
Bibliographic record
Abstract
This study applies the Mamdani Fuzzy Logic method to assess the physical fitness levels of students at SD Negeri 002 Lubuk Baja, Batam.Physical education, which has been implemented since first grade, aims to improve students' physical fitness, playing an important role in enhancing learning effectiveness.However, many students are unaware of their fitness levels, which can affect their learning performance.The sample consisted of two students, one male and one female, who were tested with Sit-Up, Squat Jump, and Running exercises.The results were categorized as 'fit' based on their heart rate before and after physical activities.Validation of the results was conducted by comparing the Mamdani output with assessments from physical education teachers and experts.The validation metrics showed 100% accuracy in classifying the students' fitness levels, based on manual calculations and MATLAB simulations.For the male student, the output was 141 (MATLAB) and 140.9967 (manual), with the fitness decision being 'fit.'For the female student, the output was 122 (MATLAB) and 121.7945 (manual), with the fitness decision also being 'fit.'This study demonstrates that the Mamdani Fuzzy Logic method is effective for assessing students' physical fitness and provides accurate decisions that align with the students' actual fitness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".