Application Of The Profile Matching Method In The Selection Of New Students For Batak Karo Bridal Makeup Skills In The PKK Program
Bibliographic record
Abstract
Makeup is the art of using makeup materials to change the natural face shape into an artistic face. Work Skills Education (PKK) itself is an education and training service program oriented towards the development of work skills provided to students in order to have competence in certain skills that are in accordance with job opportunities. Profile matching is the process of comparing the actual data value of a profile to be assessed with the expected profile value so that it can be known the difference in competence or the distance between one value and another. This research aims to facilitate the selection of new prospective students in the PKK program at the Pelawi Salon Binjai Course and Training Institute (LKP) and optimize admin work time in the process of selecting new students. By applying this method, it aims to see the eligibility of prospective students according to predetermined criteria so that the opportunity to take part in the PKK program can be received by the right person. There are 5 sample data with 6 criteria in this research, the final result in this study is 3.5 being the highest value and 2.99 being the lowest value.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".