Analisis Putusan Pengadilan Tinggi Makassar Nomor 105/PDT/2022/PT.MKS Terhadap Eksekusi Hak Tanggungan Kreditur
Bibliographic record
Abstract
One of the government's policies in the banking world is the implementation of credit provision which carries a risk of failure or congestion in credit repayment. To anticipate these risks, guarantees are needed. In banking practice, the issue of collateral is important because collateral is protection for creditors such as banks and is a form of the debtor's seriousness in fulfilling his obligations in repaying credit. The use of land as collateral for credit is based on the consideration that land is the safest and has relatively high economic value and can be tied to mortgage rights and clear and certain execution so that the funds that have been spent can immediately be returned to the creditor (bank), and these funds can be used in turnover. economic wheels. However, in practice, all the conveniences and advantages of the Mortgage Rights execution process based on Article 6 of the Mortgage Rights Law cannot always be utilized by banks as an alternative for resolving problem loans guaranteed by Mortgage Rights. There are many problematic factors that cause the mortgage rights execution process to not run as it should. The factors of this problem include various things, including the incompatibility of the legal substance of the Mortgage Rights Law which regulates the mortgage rights execution process itself, the actions and paradigms of law enforcement officials, as well as the legal culture that exists in society, including the paradigm of the debtor as the executed party. Mortgage right. This research aims to examine and analyze the process of executing Mortgage Rights in court and the considerations of the Panel of Judges in deciding the case. From the research results, it can be stated that the basis used by the judge in deciding the case is in accordance with the applicable regulations, however, in resolving bad credit itself, it would be better if you used the 3R alternative (rescheduling, reconditioning, restructuring) before carrying out the mortgage rights execution process. which then ends up in court or through judicial channels.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.017 |
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".