Penerapan Audit Sistem Informasi Pendaftaran Siswa Menggunakan Cobit 4.1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.018 |
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 it