ANALISIS KAPABILITAS APARAT PENGAWASAN INTERNAL PEMERINTAH (APIP) MENGGUNAKAN STANDART INTERNAL AUDIT CAPABILITY MODEL (IA-CM) (STUDI KASUS PADA INSPEKTORAT KOTA TEBING TINGGI) PERIODE 2017-2018
Bibliographic record
Abstract
Penelitian ini bertujuan menganalisis tingkat Kapabilitas Aparat Pengawasan Internal Pemerintah (APIP) pada inspektorat Kota Tebing Tinggi dengan menggunakan standart yang berlaku universal diseluruh dunia yaitu Internal Audit Capability Model (IA-CM) yang di bentuk oleh Auditor Sektor Publik Dunia yaitu The Institude of Internal Auditor, elemen-elemen internal audit yang mempengaruhi ketertinggalan Kapabilitas APIP dan strategi untuk meningkatkan Kapabilitas APIP Inspektorat Kota Tebing Tinggi. Penelitian ini menggunakan pendekatan penelitian deskriptif. Dalam hal menganalisis data penelitian penulis melakukan teknik pengumpulan data Observasi, dokumentasi dan wawancara. Sedangkan teknik analisi data yang digunakan adalah metode analisis deskriptif. Berdasarkan hasil penelitian berdasarkan penilaian Kapabilitas APIP menggunakan standart Internal Audit Capability Model (IA-CM) menunjukkan bahwa Inspektorat Kota Tebing Tinggi berada pada level 3 dengan catatan perbaikan (Integrated). Dari 6 elemen internal audit sesuai Standart IA-CM, 4 elemen yaitu elemen “Peran dan Layanan APIP (Service and Role of Internal Auditing)”, “Pengelolaan Sumber Daya Manusia (People Management)”, “Budaya dan Hubungan Organisasi (Organization Relationship and Culture)” dan elemen “Struktur Tata Kelola (Governance Structure)” sudah mencapai level 3 sedangkan 2 elemen lain nya yaitu elemen “Paktik Profesional (Profesional Practice)” dan elemen ” Akuntabilitas dan Manajemen Kinerja (Performance Management and Accountability)” masih mencapai level 2. Dari 6 elemen tersebut diketahui elemen yang mempengaruhi ketertinggalan Kapabilitas APIP pada Inspektorat Kota Tebing Tinggi adalah elemen yang masih berada pada level 2
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".