PEMODELAN SISTEM INFORMASI ALUMNI STMIK ADHI GUNA BERBASIS WEBSITE
Bibliographic record
Abstract
Alumni merupakan elemen penting dalam indikator perkembangan suatu lembaga pendidikan dalam hal ini universitas/sekolah tinggi yang ada sekarang ini. Oleh karena itu dalam rangka meningkatkan mutu universitas/sekolah tinggi, diperlukan suatu sistem informasi untuk mengumpulkan serta memberikan informasi data alumni yang tepat, cepat serta akurat sesuai dengan tuntutan era teknologi sekarang ini. Sesuai dengan prinsip inilah, STMIK Adhi Guna perlu menerapkan suatu sistem informasi yang dapat menghubungkan seluruh alumninya sehingga seluruh alumni dapat terintegrasi serta saling bertukar pikiran dalam banyak hal, seperti rencana temu-alumni, pengadaan aktivitas-aktivitas penting (kegiatan amal dsb), informasi penelusuran (tracing) alumni maupun informasi lowongan kerja bagi alumni yang belum mempunyai pekerjaan. Hasil dari penelitian ini adalah prototype sebagai dasar pemodelan sistem informasi alumni dengan tujuan dapat mengakomodir kebutuhan STMIK Adhi Guna dalam mengelola data alumni dengan lebih baik, sehingga nantinya dapat mempermudah komunikasi antara pihak STMIK Adhi Guna dan alumni.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.022 |
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".