Penggunaan Data Envelopment Analysis (DEA) dalam Pengukuran Efisiensi Bank Umum Syari'ah di Indonesia
Bibliographic record
Abstract
The purpose of the research is to analayze efficiency level of Sharia Commercial Bank in Indonesia (Bank Mega Syariah, Bank Muamalat Indonesia, Bank Panin Dubai Syariah, Bank BNI Syariah, Bank BRI Syariah, and Bank Syariah Mandiri) 2014-2015 period. Data used in this research is secondary data taken from Financial Statement Publication issued by Otoritas Jasa Keuangan (OJK). This research uses input-output variable with Data Envelopment Analysis (DEA) Method.The result shows the difference of efficiency score for each Sharia Commercial Bank. Based on the calculacy using Data Envelopment Analysis (DEA) Method on BUSN Foreign Exchange of Sharia Commercial Bank in Indonesia only Bank Panin Dubai Syariah that has been succeeded with 100 percent of continuously efficiency during the research. The highest efficiency is experienced by Bank BNI Syariah and BRI Syariah because during the research they experienced inefficiency. During the research Bank Mega Syariah experienced efficiency three times on quarter March 2014, March 2015, and June 2015. Bank Muamalat Indonesia only experienced efficiency twice on quarter March 2014 and March 2015, beside that Bank Muamalat Indonesia experienced inefficiency. Bank Syariah Mandiri experienced efficiency twice on quarter March 2015 and quarter December 2015.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".