Dana Pihak Ketiga dan Modal Sendiri terhadap Financing to Deposit Ratio di PT Bank BRI Syariah
Bibliographic record
Abstract
This research was conducted because the development of TPF and Equity Capital at PT Bank BRI Syariah for the 2016-2020 period fluctuated. TPF in 2016 in the third quarter and fourth quarter experienced an increase but FDR decreased as well as fluctuations in TPF during 2018 and 2020 which did not match the theory, where the higher the DPK collected, the greater the distribution of financing so that the FDR value would increase and vice versa. This study aims to determine the effect of TPF on FDR, the effect of Equity Capital on FDR, the effect of TPF and Equity Capital simultaneously on FDR at PT Bank BRI Syariah. This study uses associative quantitative research, with the object of this research being Third Party Funds, Own Capital and Financing to Deposit Ratio at PT Bank BRI Syariah. The type of data used is secondary data in the form of financial ratios published by PT. Bank BRI Syariah starting from the period 2016 to 2020. The population in this study is the financial statements of PT Bank BRI Syariah. The sample used is the quarterly published financial statements of BRI Syariah Bank for the period 2016 to 2020. The data analysis used is multiple correlation analysis, multiple regression analysis, coefficient of determination analysis, t-test and F-test. Own capital has a positive and significant effect on FDR. TPF and own capital simultaneously have a positive and significant effect on FDR at PT Bank BRI Syariah.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".