Audit Sistem Informasi Pelayanan Bpjs Kesehatan Menggunakan Framework Cobit 5.0
Bibliographic record
Abstract
BPJS Health is a legal entity formed to administer the health insurance program with the aim of protecting the entire community with affordable premiums and providing wider service coverage for the entire community. BPJS health services focus on first level health services (FKTP/first level health facilities). Due to the sensitive nature of health information stored and managed by BPJS Health, it is important to ensure that the information systems used are safe from cyber attacks and data leaks. The stages of a BPJS Health service audit are determining audit objectives, identifying services using the framework, capability level analysis, testing controls and evidence at the capability level, verifying results, and compiling audit results reports and recommendations. Audits carried out for services that focus on Process Domains DSS01 and DSS02. The measurement result is 2.5 because the value rounding index means the results are at level 3 (established process), that is, currently the processes in each process domain have been identified and standardized well so that the library service system is stable for implementation, while the expected level is at level 4 so there is a gap of 1.5. To be able to overcome the existing gap, a recommendation was made, namely to maximize the alignment of Company Operational Standards (SOP) with applicable policies.
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.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".