Pengaruh Zakat, Infak, dan Sedekah (ZIS) dan Pembiayaan Perbankan Syariah Terhadap Perekonomian di Indonesia Tahun 2012 - 2022
Bibliographic record
Abstract
This research aims to examine and analyze the influence of Zakat, Infaq and Alms (ZIS) and sharia banking financing on the economy in Indonesia, both in the short and long term. The data in this research is secondary time series data in the form of quarters from 2012 to 2022. The analysis in this research uses VAR/VECM estimation with independent variables namely Zakat, Infaq and Alms (ZIS) and sharia banking financing while the dependent variable is Gross Domestic Product (GDP) Indonesia. The VECM estimation results in this research show that in the short term Zakat, Infaq and Alms (ZIS) during the past 3 quarters had a positive and significant effect on Indonesia's Gross Domestic Product (GDP), then in the long term Zakat, Infaq and Alms (ZIS) during 1 quarter ago had a positive and significant impact on Indonesia's Gross Domestic Product (GDP). Meanwhile, sharia banking financing (IBF) in the short term did not have a significant effect on Indonesia's Gross Domestic Product (GDP), while in the long term sharia banking financing (IBF) 1 quarter ago had a positive and significant effect on Indonesia's Gross Domestic Product (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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".