IMPLEMENTASI CONTROL OBJECTIVES FOR INFORMATION AND RELATED TECHNOLOGY TERHADAP AUDIT INTERNAL DI INDONESIA
Bibliographic record
Abstract
Sistem control objectives for information and related technology is very advanced and has developed many advanced applied in countries such as Germany, Canada, United States of America, United Kingdom,etc. A lot of auditor use Control objective for information and Related Technology to understanding client’s company or organisation structure and internal control system, futhermore COBIT helps auditor to reveal about every single detail of it, such as their real expenses, benefit, deals and goals. This study aims to describe the system control objectives for information and related technology, as well as linkages with COBIT internal audit system. In addition, this study also aims to inform companies in Indonesia which are some who have implemented COBIT in their internal control systems. The result about this study is with the use of COBIT system in the company, it will be easier to achieve their goals as well as easier to avoid the possible risks that can be encountered. And for the auditor, COBIT greatly help facilitate auditor in view of the level of compliance, transparency, as well as the successful implementation of the system has been made. Key Words : Internal Audit, Control Objectives and Related Technology, IT Governance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".