PERANCANGAN TATA KELOLA TEKNOLOGI INFORMASI PADA PERGURUAN TINGGI DENGAN MENGGUNAKAN FRAMEWORK COBIT 5 STUDI KASUS : STMIK DHARMA WACANA METRO
Bibliographic record
Abstract
STMIK Dharma Wacana Kota Metro sebagai lembaga pendidikan berupaya untuk mengikuti perkembangan dalam menerapkan teknologi informasi. Namun saat ini tata kelola teknologi yang diterapkan tidak berjalan sesuai harapkan perguruan tinggi, hal ini terlihat dari pengguna yang kurang memahami pemakaian perangkat komputer/teknologi serta belum adanya prosedur dalam pemakaian dan perbaikan pada teknologi, kegagalan pengoperasian system, hilangnya data oleh virus, pemakaian komputer yang bukan pemiliknya sehingga rentan dalam bocornya informasi, kurangnya pemahaman staff tentang teknologi komputer yang digunakan (komputer), staff yang melakukan pekerjaan diluar unit kerjanya Oleh karena itu, dibutuhkan suatu pengelolaan terhadap aktivitas bisnis dan resiko yang tidak hanya meliputi masalah teknis atau operasional, tetapi juga eksekutif manajemen agar dapat memenuhi kebutuhan bisnis, seperti IT governance
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 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".