Analisis Tingkat Efisiensi Bank Pembiayaan Rakyat Syari’ah (Bprs) di Kota Yogyakarta Menggunakan Data Evelopment Analysis (Dea) Periode 2017
Bibliographic record
Abstract
This research is entitled "Analysis of Efficiency Level of Intermediation Function of Syari'ah People's Financing Bank (BPRS) in Yogyakarta City Using Data Evelopment Analysis (DEA) Period 2017". The purpose of this research is to analyze the efficiency level of Syari'ah People's Financing Bank (BPRS) in Yogyakarta City which has been confirmed by government among Shariah Banks of Shari'ah used in this research consists of 4 BPRS. Among them are BPRS Berkah Dana Sejahtera, BPRS Dana Hidayatullah, BPRS Mitra Harmoni Sejahtera, dan BPRS Unisia Insan Indonesia. The data used in this study is secondary data taken from www.ojk.co.id published by each Sharia Bank Financing (BPRS). This research uses input-output variable with Data Evelopment Analysis (DEA) method. From the results of research conducted shows that in Quarter I - Quarter IV only got one Sharia Bank Financing (BPRS) that experienced inefficiency. Based on the calculation using Data Evelopment Analysis (DEA), in Quarter I - Quarter II BPRS Berkah Dana Sejahtera \nexperienced inefficiency, whereas in Quarter I – IV BPRS Dana Hidayatullah, BPRS Mitra Harmoni Sejahtera, dan BPRS Unisia Insan Indonesia showed efficiency. The period 2017 Quarter I - fourth quarter III of Syari'ah People's Financing Bank (BPRS) namely BPRS Dana Hidayatullah, BPRS Mitra Harmoni Sejahtera, dan BPRS Unisia Insan Indonesia already 1.000 or experienced efficiency continue to manage during the quarter.
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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 | 0.003 | 0.005 |
| 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) | 0.007 | 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".