PENERAPAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) PEMILIHAN MAHASISWA TERBAIK PADA STMIK DHARMA WACANA
Bibliographic record
Abstract
Dalam perguruan tinggi mahasiswa diminta untuk aktif dan berprestasi dalam bidang akademik maupun non akademik. Untuk itu dalam penentuan mahasiswa terbaik tidak hanya dilihat darri nilai akhir IPK tetapi juga dinilai dari kemampuan lainya. Pada STMIK Dharma Wacana pemilihan mahasiswa terbaik masih dilakukan dengan cara manual yang mana proes ini memakan waktu yang lama dan memungkinkan terjadikan kesalahan dalam proses penilaian. Tujuan dari penelitian ini adalah dengan menerapkan metode Simple Additive Weighting (SAW) agar mendapat hasil mahasiswa terbaik di STMIK Dharma Wacana pada masing-masing program studi, dengan berdasarkan sekala perhitungan yang memiliki nilai presentase sebagai berikut: IPK 20%, Masa Studi 20%, Tidak ada Nilai D 10%, Kegiatan Kemahasiswaan 20%, dan Prestasi Lain 10%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.059 |
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; both teacher heads agree on what is shown here.
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".