9. PENGEMBANGAN MANAJEMEN INFORMATION AND COMMUNICATION TECHNOLOGY DI AKADEMI ANGKATAN UDARA
Bibliographic record
Abstract
Rencana penelitian ini dilatar belakangi oleh kurang optimalnya fasilitas ICT(Information and Communication Technology) di AAU dalam mendukung kegiatan belajar mengajarTaruna, fenomena yang terjadi ketika pandemi covid-19 Taruna harus di isolasi di Flat, fasilitasICT berupa Smart Class tidak dapat mendukung kegiatan belajar mengajar Taruna. Sehinggadirumuskan masalah pengembangan manajemen ICT yang mampu meningkatkan mutupembelajaran di AAU. Adapun teori yang relevan dengan fokus penilitian yaitu teori tentang ICT,Manajemen, Pembelajaran dan konsep perkembangan ICT 5.0. Metode penelitian yang digunakanadalah metode kualitatif dan menggunakan metode triangulasi untuk memvalidasi data. Narasumber dalam menentukan konsep pengembangan ICT yang diharapkan yaitu personel yangterlibat langsung dalam pemanfaatan dan pengelolaan fasilitas ICT tersebut. Hasil pembahasandari penelitian berupa konsep pengembangan ICT di AAU yang diinginkan berdasarkan teoridan konsep serta kebutuhan pengguna sehingga fasilitas ICT yang akan dikembangkan dapatdimanfaatkan dalam meningkatkan mutu pembelajaran di AAU.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.064 | 0.026 |
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".