PENGARUH RISIKO LIKUIDITAS, RISIKO KREDIT, RISIKO PASAR DAN RISIKO OPERASIONAL TERHADAP RETURN ON ASSETS (ROA) PADA BANKUMUM SWASTA NASIONAL DEVISA
Bibliographic record
Abstract
The Study Entitled Business Risk Influence Toward ROA ( Return On Asset ) In The Foreign Exchange National Private Banks Go Public. In this study aims to determine whether the LDR, IPR, NPL, IRR, BOPO, and FBIR have a significant impact on ROA jointly and individually for state banks in the beginning of the period first quarter 2010 to fourth quarter year 2013. Data and data collecting method used in this research is secondary data source from quarterly financial statement from Foreign Exchange national private banks go public Financial statement appendix researched from quarterly financial statement I 2010 until quarterly financial statement IV 2013. Data analysis technique used in this research in regression analysis, F-test and T-test.Use of the analysis carried out for the steps in calculating financial ratios and analysis to test the hypothesis. Based on the calculation result known that the LDR, IPR, NPL, IRR, BOPO and FBIR, jointly simultan in the Foreign Exchange National Private Banks Go Public Key Words:Banking business risk, Regression analysis, Business Risk Influence Towards ROA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.008 | 0.001 |
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".