The Internal Model of Default Credit for Rural Banks in Indonesia
Bibliographic record
Abstract
The urgency of the internal model of default credit for the rural bank in Indonesia is increasing due to the recent acceleration of the increase in Non-Performing Loans at rural banks. This research will formulate an internal model of default credit that can reduce the level of default risk in rural banks based on the explanatory sequential design of mixed research method. The findings of quantitative analysis integration by Chi-Square Analysis, Discriminant Analysis, and Logistics Regression analysis at BPR BKK Pekalongan Regency in 2021 will be explored further by qualitative research. Using nine variables that affect debtors' failure, this study finds that the interest rate is a variable that consistently affects the status of default loans using the integration of 3 analyses and qualitative analysis. The study results indicate that rural banks need to pay more attention to determining credit interest rates when prospective debtors apply for credit. The determination of interest rates is related to compensation for risks faced by rural banks in connection with asymmetric information about the debtor's ability to pay while considering the interest rate determined by the IDIC.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.000 |
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.
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".