DRSA: A New Framework for Reliability Audit of Electronic Transaction Document Security in Electronic-Based Government Information Systems in Indonesia
Bibliographic record
Abstract
An electronic-based government system (EBGS) facilitates access to public services and accelerates both administration and decision-making processes.However, with the increasing complexity of information systems, ensuring system reliability becomes a critical aspect to maintain services that are consistent, secure, and error-free, thereby safeguarding both the community and the government.This study aims to develop a comprehensive and practical information system reliability audit model for EBS in Indonesia.The audit model proposed in this study involves various components such as authentication, access control, audit trails, and disaster recovery, all of which play essential roles in ensuring the reliability and security of government information systems.The result of this study is a framework for ensuring the reliability of personal data security and electronic transactions within EBS.By adopting this model, it is anticipated that government agencies can mitigate potential risks in their systems and implement more structured and measurable improvement steps, thereby increasing stakeholders' trust in their information systems.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".