Decision making for determining promotional targets for the STMIK Kaputama campus using the Promethee method
Bibliographic record
Abstract
Every foundation or educational institution certainly has efforts to maintain its existence amidst competition from educational institutions that continue to innovate to attract public interest in an educational institution . The results of good and appropriate promotion can be seen from the development of student admissions each year. To carry out promotions, of course you have to pay attention to things such as the type of school, travel time, number of computer science enthusiasts. Apart from that, determining promotional targets to get good, effective and efficient results. So a decision-making system is needed that is able to assist in the analysis of determining promotional targets at STMIK Kaputama. With the existence of a decision support system and based on STMIK Kaputama promotion target criteria, we are able to get the right promotion target results for the advancement of STMIK Kaputama development and realizing the vision and mission for the future, apart from that, so that prospective STMIK Kaputama students increase, because of the right promotion targets . To make decisions effective and efficient, this decision making system was built using the Promethee method , which is one of the decision making methods used to obtain a problem solution. Promethee is used to determine and produce decisions from several alternatives. From the results of the research conducted, it was found that the Promethee method was able to produce the best concise decisions.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".