The Effect of TPF, NPF and CAR on Profitability with Financing as an Intervening Variable in Indonesian Islamic Banks during the Covid-19 Pandemic
Bibliographic record
Abstract
The aim of this research is to determine the effect of TPF, CAR, NPF on profitability with financing as an intervening variable. This research uses a quantitative type of research using PLS analysis. This research uses quantitative research. The research was conducted during the Covid 19 pandemic in 2020, quarter 4 to 2022, quarter 1. The data population for this research was 14 Islamic commercial banks during the Covid 19 pandemic. The sampling method used was purposive sampling, namely using several criteria for the 5 Islamic commercial banks that were sampled. Study. The results that influence this research are NPF on profitability, CAR on profitability and TPF on financing. The high value of Third Party Funds (TPF), Non Performing Finance (NPF), and Capital Adequacy Ratio ( CAR) will affect the business activities of the banks, which will certainly affect the level of profitability. Meanwhile the most important and largest bank business activity is financing.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".