PENGEMBANGAN SISTEM INFORMASI AKADEMIK MENGGUNAKAN METODE UNIFIED MODELING LANGUAGE BERBASIS WEBSITE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pengembangan sistem informasi akademik bertujuan untuk memberikan sarana dasar kepada perguruan tinggi. Sistem informasi akademik diperlukan untuk mengelola seluruh kegiatan akademik diantaranya mengelola kartu rencana studi, mengelola kartu hasil studi, mengelola data dosen dan tenaga kependidikan, mengelola data mahasiswa, mengelola kelas, mengelola pertemuan dan presensi. Memiliki sistem informasi akademik akan mengurangi resiko keamanan data serta mendukung kemandirian pengelolaan teknologi informasi. Pengembangan sistem informasi ini menggunakan metode penghimpunan data menggunakan metode tanya jawab (interview) dan pengamatan lapangan. Performance, Information, Economics, Control, Efficiency dan Service adalah metode PIECES yang akan digunakan dalam menganalisa sistem. Unified Modelling Language (UML) adalah metode perancangan sistem yang digunakan dalam penelitian ini. System engineering, Requirement analysis, Design, Coding, Testing dan Maintenance adalah metode pengembangan sistem Waterfall yang peneliti gunakan. Sistem informasi akademik dikembangkan sesuai kebutuhan institusi saat ini tetapi seiring perkembangan peraturan dan kebutuhan institusi maka perlu dilakukan evaluasi kinerja secara periodik.
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.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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