Identifying Student Interests in the Vocational Field Using the Certainty Factor Method
Bibliographic record
Abstract
SMK Negeri 1 Kota Binjai is a vocational high school that has several competency skills majors. This school has an interest in applicants who are quite interested every year, the number of applicants in admitting new students every year continues to increase from year to year. Vocational education is an educational model that focuses on individual skills, skills, work habits, and appreciation of the jobs needed by people in the business/industry world. Lack of information about talent interests and career paths or vocational education greatly affects students in making choices regarding majors. Many students who choose majors are not interested in their talents and other reasons. This can make students wrong in taking a major which causes inadequate competence of students in completing their education and will certainly affect the future of these students. expert system which is a computer program, which is able to store knowledge and rules like an expert. With the existence of an expert system, each student is able to identify and find out what areas of expertise he is interested in. The Certainty Factor method is a method for proving whether a fact is certain or uncertain in the form of a metric which is usually used in expert systems. From the results of trials conducted by the expert system to identify students' interest in the vocational field using the Certainty Factor method, the highest score is majors Online Business and Marketing with a confidence value of 89.67%.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".