The Influence of Internal and External Factors on NPF And NPL
Bibliographic record
Abstract
Indonesia has two types of bank, islamic banking and conventional banking. In their activities, banks are often facing any risks, named financing risk (NPF) in the islamic banking and credit risk (NPL) in the conventional banking. Based on data by OJK, the value of NPF is always higher than NPL. However, in January-August 2020 the NPF tended to decrease while the NPL tended to increase, even indicating a movement that would excited the NPF value. Therefore, it's necessary to the research of the factors that influence both NPF and NPL, including the internal and external conditions of the bank. The data that used as reference is the secondary data from OJK of 10 both islamic and conventional commercial banks from the first quarter of 2019 to the third quarter of 2020. Furthermore, the data is analyzed with panel model fixed effect data analysis with the robust standard error estimation method and panels corrected standard error (PCSE cross-sectional SUR). By using 5% of significance level, this research results that NPF is only significantly and positively influenced by FDR. However, NPL is significantly and negatively affected by the inflation and ROA, also significantly and positively influenced by CAR, LDR, and BOPO.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".