Implementasi Metode Simple Additive Weighting Untuk Penerimaan Mahasiwa Baru Jalur KIP Pada STMIK Dharma Wacana
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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 it