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Audit Sistem Informasi Pelayanan Bpjs Kesehatan Menggunakan Framework Cobit 5.0

2024· article· en· W4404493175 on OpenAlexaff
Nurhafieza Nurhafieza, Dila Aulia Putri, Nurfadillah Nurfadillah, Evi Vusvitasari

Bibliographic record

VenueJurnal Sistem Informasi dan Ilmu Komputer · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsCOBITAuditBusinessComputer scienceInformation technologyOperating systemAccounting

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0040.007
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.222
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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