Penerapan Audit Sistem Informasi Pendaftaran Siswa Menggunakan Cobit 4.1
Bibliographic record
Abstract
The registration system at BLK Surakarta appears to be experiencing data redundancy which needs to be addressed through in-depth analysis. The student registration process is integrated into the system, but there are still deficiencies in data management which results in frequent data duplication or errors. This research uses Cobit 4.1 as a framework for auditing the registration system at BLK Surakarta, with a focus on the Delivery and Support subdomain (DS 10 and DS11). The main objective is to assess the maturity level of the IT processes implemented at BLK Surakarta and provide recommendations for improvement. The research results show the need for BLK Surakarta to carry out regular system performance evaluations, involving parties responsible for identifying and overcoming problems that arise, in order to ensure optimal system conditions. Evaluation in Domain 10 Delivery & Support shows a current maturity level of 3.27, while in Domain 11 Delivery & Support, the maturity level is 3.31. However, it was found that the process of managing payment data for new student registration was less than optimal due to limited tools available.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".