Islamic bank efficiency: an efficiency method with SFA
Bibliographic record
Abstract
Judging from the financial ratios, the performance of Islamic banking in Indonesia was remarkably stable both before and during the Covid-19 pandemic. However, another thing is whether this condition could make Islamic banks continue to work effectively. This study aimed to measure the cost efficiency of Islamic commercial banks in Indonesia quarter I of 2019 – quarter IV of 2020 and analyze the influencing factors in cost efficiency. The study used a saturated sampling technique with a total sample of 14 Islamic commercial banks, while the efficiency level was determined using the Stochastic Frontier Analysis (SFA) method. It turns out that PT. Bank Muamalat Indonesia Tbk. has the highest efficiency value of 0.9284. Several banks with an efficiency value of more than 0.5 are PT. Bank Aceh Syariah, PT. Bank BNI Syariah, and PT. Bank Mega Syariah. In this study, only inflation variables affect efficiency. In contrast, bank size, Return on Assets (ROA), Net Operating Margin (NOM), Non-Performing Financing (NPF), Financing to Deposit Ratio (FDR) variables, Capital Adequacy Ratio (CAR), Gross Domestic Product (GDP), and the rupiah exchange rate don’t affect the efficiency. Overall, all the company's internal variables and environmental variables affect efficiency.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".