Determining The Selection Of Departments At Abdi Negara Vocational School Using The Additive Ratio Assessment (Aras) Method
Bibliographic record
Abstract
Along with the occurrence of competition and the development of technology and information in the current era of globalization requires skilled and ready-to-use human resources in the world of work. The efforts made are to improve the quality of education in Indonesia which always receives attention from various parties. One way to improve education is to determine the right majors at Vocational High Schools (SMK). The differences in each student with a different background must be considered because they can determine whether student achievement is good or bad. In ddition, the decision also greatly influences the alternative process chosen, especially in choosing the concentration of majors that are in accordance with the skills and expertise of students. Based on the author's observations at ABDI NEGARA VOCATIONAL SCHOOL through data collection both by conducting interviews and through available documents, the reasons students choose majors are usually based on student parents' references, besides that due to trend reasons (most students take that major). Therefore, through research using Decision Support Systems, it is hoped that it can provide recommendations to find out which majors to choose according to the interests or abilities of each student. So that there are no problems regarding failure or dropping out of school (drop out).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".