Analisis Kontribusi Perbankan Syariah Terhadap Pertumbuhan Ekonomi Indonesia
Bibliographic record
Abstract
The purpose of this study was to determine the effect of the dependent variables of Total Assets, Third Party Funds, Financing (mudharabah, musyarakah, murabahah), and ZISWAF on Indonesia's Economic Growth represented by GDP as dependent variable. The data used is financial reports for the 1st quarter of 2017 to the 4th quarter of 2020 sourced from Islamic Commercial Banks, Sharia Business Units, the Central Statistics Agency, and the Financial Services Authority. The method used is Autoregressive Distributed Lag (ARDL) to see the long-term and short-term effects of the independent variable on the dependent variable. The results of the study show that Total Assets and ZISWAF have a significant positive effect in the long and short term on GDP. Third Party Funds have a significant long-term and short-term negative effect on GDP. Financing has a significant positive effect in the long term but in the short term has a significant negative effect on GDP. In addition, simultaneously all independent variables have a significant positive effect on GDP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".