How the Rules of no.11/POJK.03/2020 Banking Restructuring Policy Improve Financial Performance? (Empirical Study of Islamic Banks in Indonesia)
Bibliographic record
Abstract
The Covid-19 pandemic has significantly impacted the economy, including the banking industry.The impact on the banking industry is a decline in the health of banks.One form of bank soundness assessment can be seen from the movement of financial ratios, including Non-Performing Financing (NPF), Capital Adequacy Ratio (CAR), Return on Assets (RoA), and Operational Expenditure to Operating Income (BOPO), and Financing to Deposits Ratio (FDR).This study aimed to examine the impact of the implementation of banking restructuring policies on the financial performance of Islamic Commercial Banks in Indonesia.This study used an observation period of 36 months, calculated 1 year before and after the implementation of rules No.11/POJK.03/2020.The sampling method used purposive sampling with 119 observational data samples.Hypothesis testing used the independent Mann-Whitney t-test since the data were not normally distributed.The results showed that the banking restructuring policy could only improve the bank's financial performance, namely CAR and FDR, but not the ratio of NPF, ROA, and BOPO.The contribution of this study can be used as one of the basics for assessing the effectiveness of implementing government policies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".