PENGEMBANGAN SISTEM PELACAKAN ALUMNI (TRACER STUDY) MENGGUNAKAN METODE PROTOTIPE BERBASIS WEBSITE
Bibliographic record
Abstract
Pelacakan alumni (tracer study) menjadi salah satu kegiatan yang sangat penting penting untuk dilakukan secara berkala dan konsisten. Pelacakan alumni sebenarnya merupakan salah satu bagian dari pengembangan universitas atau perguruan tinggi yang berkelanjutan. Hasil pelacakan alumni yang baik akan menghasilkan inputan yang baik pula pada universitas khususnya program studi untuk mengembangkan kurikulum dan model pembelajarannya. Sistem pelacakan alumni dibangun berbasis website dengan metode pengembangan sistem prototipe. Pengembangan sistem dimulai dengan pengumpulan data pada objek dan pengguna melalui metode wawancara dan observasi. Setelah itu dilakukan analisa sistem menggunakan metode PIECES dan dilanjutkan dengan perancangan sistem menggunakan metode Unified Modelling Language (UML). Penelitian ini menghasilkan model awal sistem pelacakan alumni yang kedepannya akan terus dikembangkan sesuai dengan perkembangan peraturan dan kebutuhan penggunanya.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".