Implementasi Metode Simple Additive Weighting Untuk Penerimaan Mahasiwa Baru Jalur KIP Pada STMIK Dharma Wacana
Bibliographic record
Abstract
KIP scholarships at stmik have 7 (seven) types of scholarships given to prospective students such as KIP scholarships, achievement scholarships, Islamic boarding schools, CSR, DT Cares, Creator Content, and Tahfhidz Qur'an,There are registration problems and limited quotas, so it is necessary to have a selection of calculations and election data collection in order to produce accurate data. Of the several types of scholarships, the KIP scholarship is the scholarship most in demand by prospective STMIK Dharma Wacana students. Therefore, this research will focus on KIP scholarships. The method used for this problem is Simple Additive Weighting (SAW). The SAW method is expected to be able to select the best alternative from a number of alternatives.The criteria for accepting registration for prospective new students through the KIP route include: Written Test, Interview Test, Report Card Score Test, File Completeness Score, and Home Eligibility Value. The result of calculating the Simple Additive Weighting method is that the highest score is Ar-Roqiib'u Raihannicko, the second highest is Eka Diana Forensia, and the third highest is Ahmad Rendi Ardiyanto.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".