Optimization of Profit-Sharing Financing at Islamic Banking in Indonesia
Why this work is in the frame
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Bibliographic record
Abstract
The purpose of this study is to identify factors that can encourage an increase in profit- sharing financing. These factors are third-party funds in the form of mudharabah deposits, non-performing financing, equivalent rate, operational efficiency ratio, economic growth, and inflation. The research method uses a co-integration and error correction model (ECM) with a sample of the Islamic banking industry in Indonesia from the first quarter of 2015 to the third quarter of 2020. The results show that the factors that encourage profit-sharing financing are the growth of third-party funds in the form of mudharabah deposits, non- performing low funding, low equivalent rate, operational efficiency, and economic growth. These factors are the key to driving the growth of profit-sharing financing. This research contributes to providing various alternative strategies in encouraging the growth of profit- sharing financing, such as increasing retained earnings from profit, providing attractive profit-sharing incentives, transparency of financial reports to attract people to invest in Islamic banks, prevention and supervision of non-performing financing, be careful in determining the ratio by taking into account several internal and external aspects, as well as paying attention to the movements of existing economic growth. DOI : https://doi.org/10.26905/jkdp.v25i2.5212
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it