Pengaruh Risiko Usaha terhadap Permodalan pada Bank Umum Swasta Nasional Devisa
Bibliographic record
Abstract
CAR is one of indicators that used to measure bank capital adequacy. Capital for banks is used to absorb losses originating from banking activities, and as a basis for several policies issued by Bank Indonesia. The purpose of this study is to determine effect the independent variables LDR, IPR, NPL, APB, IRR, PDN, FBIR, dan BOPO both simultaneously and partially have a significant effect on CAR and which variable is the most dominant effect on CAR. This study uses secondary data taken from financial statements from the first quarter of 2014 to the second quarter of 2019 at the Foreign National Private Commercial Banks. The sample consisted of Bukopin Bank, Woori Saudara 1906 Bank and Sinarmas Bank. Data is processed using SPSS Statistics 2.1 for windows and F test to see the effect simultaneously and t test to see the effect partially. The results show that LDR, IPR, NPL, APB, IRR, PDN, FBIR, dan BOPO simultaneously have a significant effect on CAR. IPR and PDN partially has a unsignificant negative effect on CAR. NPL partially has a negative significant effect on CAR. IRR and partially has a posittive significant effect on CAR. LDR, APB, BOPO, and, FBIR partially has a unsignificant posittive effect on CAR. The most dominant is the IRR of 18,6624 percent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".