Non Performing Loan Sebagai Pemoderasi Pengaruh Kredit yang Disalurkan Terhadap Profitabilitas pada Bank Pembangunan Daerah
Bibliographic record
Abstract
This study aims to obtain empirical evidence effect of lending on profitability, and the influence of lending on profitability with Non Performing Loan (NPL) as moderator at Regional Development Bank. The sampel of the research, namely: BPD DKI, BPD Jawa Barat dan Banten, BPD Jawa Tengah dan BPD Jawa Timur. Data and collecting data method in this research is data which is taken from financial report of Convetional Regional Development Banks. Bank started from the first quarter period of 2013 until to the second quarter period of 2018. The technique of data analyzing is descriptive analyze and using Moderated Regression Analysis (MRA). The results showed that lending a negative effect on profitability, while NPL positive influence on the relationship between loans extended to profitability. Keywords : Loans Disbursed, Profitability, and Non Performing Loan.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".