Studi Literatur: Audit Sistem Informasi Menggunakan Kerangka Kerja Cobit 5
Bibliographic record
Abstract
Audit sistem informasi mencakup tinjauan yang cermat terhadap risiko yang berkaitan dengan sistem dan proses informasi dalam organisasi. Selain itu, audit ini juga mengevaluasi apakah kontrol yang ada cukup untuk menjamin efisiensi, efektivitas, integritas, serta keamanan data dan aset sistem informasi organisasi. Secara umum, audit membantu organisasi dalam memantau dan mengevaluasi kinerja bisnis, serta melindungi kepentingan manajer, karyawan, pelanggan, dan investor. COBIT 5 menawarkan praktik terbaik di berbagai area dan kerangka kerja proses dalam struktur yang mudah dikelola, membantu mengoptimalkan investasi. Dalam penelitian ini, ditemukan beberapa makalah mengenai audit TI/SI dengan pendekatan COBIT 5 yang memiliki perspektif berbeda. Dari 7 artikel yang diperoleh secara acak, terdapat tiga artikel yang berjudul audit sistem informasi dan membahas proses audit, sedangkan 4 artikel lainnya juga berjudul audit sistem informasi tetapi lebih fokus pada tingkat kematangan tanpa membahas proses audit. Penelitian ini diharapkan dapat memberikan wawasan baru bahwa audit sistem informasi dan tingkat kematangan adalah dua hal yang berbeda, yang juga dianalisis dalam kerangka COBIT 5.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".