Analisis Sistem Pengendalian Internal Melalui Audit Berbasis Risiko (ABR) Oleh Aparat Pengawas Intern Pemerintah (APIP) Dalam Mencapai Target Rencana Pembangunan Jangka Menengah Daerah (RPJMD) Studi Kasus pada Inspektorat Kota Banjarbaru
Bibliographic record
Abstract
This research explains the role of APIP in carrying out, its functions and duties to achieve the target of Regional Medium Term Plan (RPJMD) 2016-2020. Inspectorate of Banjarbaru City as an element of local government oversight is required to act as an institution capable of improving the quality of supervision on the way of regional development, so as to realize good governance and clean governance. Quoted from the RENSTRA Inspektorat Banjarbaru City there are still strategic issues that can hamper the implementation of the vision mission RPJMD mission in Inspectorate Banjarbaru City. These strategic issues include many findings and recommendations of the results of the investigation that have not been followed up, the lack of strict sanctions on the management of performance andinternal control is not good, the quality of public services should still be improved, supervision procedures have not run well, commitment and motivation APIP still needs to be improved. In addition, the Inspectorate of Banjarbaru City has limited resources so it must be able to work efficiently and effectively. as well as the non- implementation of Risk-Based Audit approaches for all functional functional officials. Through an effective internal control system and effective RSP audit implementation is expected to assist APIP's role in implementing the RPJMD 2016- 2020 to reduce strategic issues and mitigate risk to acceptable limits.Keywords:Risk Based Audit (RBA), Government Internal Control System (SPIP), Inspectorate, Good Governance(GC), Clean Governance (CG), Government Internal Supervisory Apparatus (APIP)
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".