Pengaruh Perbankan Syariah terhadap Pertumbuhan Ekonomi Indonesia
Bibliographic record
Abstract
The purpose of this research is to determine the effect of total assets, financing provided, Third Party Funds (TPF), Non Performing Financing (NPF) on Indonesia's economic growth for 6 (six) years from 2017 to 2022. This research using quarterly secondary data from the first quarter of 2017 to the fourth quarter of 2022 sourced from Sharia Banking Statistics (SPS) published by the Financial Services Authority (OJK) for data on assets, Financing Provided, Third Party Funds, and Non Performing Financing (NPF). Meanwhile, economic growth data is measured using Gross Domestic Product (GDP) data from the Indonesian Central Statistics Agency (BPS). The data processing technique used in this research is multiple linear regression using Eviews version 12 software to determine the relationship between the dependent variable and the independent variable. The research results show: 1) Partially Total Assets and Financing Provided (PyD) have a significant and positive influence on Indonesia's economic growth. 2) Partially, Third Party Funds (TPF) and Non Performing Financing (NPF) have a insignificant influence on Indonesia's economic growth. 3) Simultaneously Total Assets, Financing Provided (PyD), Third Party Funds (TPF), and Non Performing Financing (NPF) have a significant positive influence on Indonesia's economic growth
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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 | 0.001 | 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) | 0.008 | 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".