ANALISIS PENGARUH KINERJA KEUANGAN DAN EKONOMI \nMAKRO TERHADAP PENYALURAN KREDIT PERBANKAN DI \nINDONESIA PERIODE 2010-2019
Bibliographic record
Abstract
Credit distribution constitutes a banking capability in providing or implementing a loan under payment agreement executed at the beginning of agreement within period of time agreed by both parties. This study aimed to analyze the effect of financial performance and macro in banking sector on the of banking credit distribution in Indonesia. This study used secondary data in quarter I-IV during the period of 2010-2019, derived from official website of Financial Services Authority (www.ojk.go.id) and Statistics Indonesia (Badan Pusat Statistik (www.bps.go.id)). Analysis technique in processing data used analysis of panel data regression using a software of statistics program Eviews10. \nResult of this study suggested that Third Party Funds (DPK), Operating Expenses and Operating Income (BOPO), Loan to Funding Ratio (LDR), and BI Rate simultaneously affect the of credit distribution in banking particularly in Indonesia with calculated-F value of (11132.4) > (2.41). The result of t test revealed that DPK and LDR partially promoted significant positive effect of banking credit distribution, then BOPO promoted significant negative impact of banking credit distribution in Indonesia. Subsequently, for BI Rate, it did not have significant effect on banking credit distribution in Indonesia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".