Pengaruh Risiko Usaha terhadapReturn On Asset (Roa) pada BankPembangunan Daerah
Bibliographic record
Abstract
The purpose of this research is analyzing whether LDR, NPL, IRR, FBIR and BOPO has a significant influence simultaneously and partially on Bank Pembangunan Daerah (Regional Development Bank). Samplesof this research are five banks : BPD Aceh , BPD Bali, BPD West Sumatera, BPD West Sumatera, BPD South Sulawesi dan West Sulawesi and South Kalimantan. Data is a secondary data and data collection method in this research is collection data from publication financial report of Regional Bank in Bank Indonesia website, starts from the first quarter period of 2009 until the second quarter of 2012. Data analysis technique in this research is descriptive analysis and multiple linear regression. Based on the accounting and result by using SPSS 16.0 for windows, is shows that LDR,NPL, IRR, FBIR has significant influence simultaneously on ROA at Regional Development Bank. BOPO partially has negative and no significant influence on ROA at Regional Development Bank. Keywords : LDR, NPL, IRR, FBIR and BOPO on 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".