Penggunaan Metode Logika Fuzzy Mamdani untuk Menentukan Potensi Bakat dan Keterampilan Siswa
Why this work is in the frame
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Bibliographic record
Abstract
Educational management in the implementation of the educational process in high schools wants its graduates to get jobs or continue their studies later according to their abilities. But in reality it is not as beautiful as expected, some alumni students who continue their studies to college drop out of their studies because the majors they take at college do not match the interests and talents of the students or do not match the abilities of the students, so that it is very important to find out the intelligence, interests and talents of the students early so as not to be late in recognizing and developing the potential of the students based on the intelligence, interests and talents of each student. This study aims to identify talents that are more dominant than the skills possessed by students by calculating the Fuzzy Logic method which can help students determine majors related to their talents after graduating from school, so that it can reduce cases of wrong majors faced by students after determining their majors at college. The problem solving used in this study uses the Fuzzy Logic method with the aim of determining the most dominant skill value of a student against the criteria of Sports, Language, Communication, Writing, and Singing based on alternative skills of Physical Fitness, Music, Social, Art, and Leadership. This research will produce the best rule that is expected to be used as a Decision Support System in determining talent based on student skills that can be used as a recommendation to determine the major to be chosen in college. The results obtained in this study are to determine the most appropriate rule and 5 rules are obtained for the application of Fuzzy Logic to determine the most dominant talent from student skills.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it