Optimization of Profit-Sharing Financing at Islamic Banking in Indonesia
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".