ANALISIS POTENSI TINDAK PIDANA KORUPSI DALAM PELAKSANAAN TUGAS DAN JABATAN NOTARIS DAN PEJABAT PEMBUAT AKTA TANAH
Bibliographic record
Abstract
This research is raining to know and recognize: (1) Any actions that have potential as corruption in the execution of duties and positions of Notary and PPAT (2) the efforts made by Notary and PPAT in preventing their actions to not have the potential to be criminal act of corruption This research is an empirical normative research because in this study the authors combine normative legal research (secondary data) with empirical legal research (primary data obtained from research results in the field). The source of this research is Notary in the working area of Jambi city and academician. The sampling technique used is purposive sampling. The method of analysis used in this study is the method of thinking qualitative analysis and drawing conclusions using inductive thinking methods. The results of this study indicate that potential acts as corrupt offenses in the performance of duties and positions of Notary and Officials of the Deed of Land are contained in Article 2, 3, 5, 10 and Article 12 Sub-Article H of Law Number 20 Year 2001 concerning the amendment of Law Number 31 Year 1999 concerning the Eradication of Corruption. Several articles indicate the potential of Notary and PPAT may be subject to those articles if they commit a criminal act of corruption. In the case of the prevention of his actions so as not to have the potential to act Corruption is to work professionally in accordance with the applicable Law, and Notary Code of Ethics and PPAT.Keywords: Notary Public, PPAT, Corruption
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".