Pertumbuhan Ekonomi Indonesia Perspektif Ekonomi Islam: Peran Inflasi, Pengeluaran Pemerintah, Hutang Luar Negeri dan Pembiayaan Syariah
Bibliographic record
Abstract
Economic growth is a fundamental indicator in assessing economic performance. In assessing the economic growth, it can use several important variables such as Inflation, Government Consumption Expenditure, Foreign Debts, and Sharia Finance. In turn, this research aims to analyze the impacts of these variables on the economic growth in Indonesia (Quarter I – Quarter IV) in the period of 2011-2018. The Error Correction Model used in the analysis method. The results of the analysis showed that the variable inflation in the long-term harmed economic growth; while, in the short-term, the level of inflation had a positive impact on economic growth. Meanwhile, the variable of Government Consumption Expenditure had a negative contribution to economic growth. Furthermore, foreign debt in the long term hurt economic growth, but for the short term, it could bring the positive one. Variable of Sharia finance showed a good result both in the short term and in the long term with a negative correlation with economic growth in Indonesia. However, all variables of inflation, Government Consumption Expenditure, foreign debts, and sharia finance simultaneously had an impact on National 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 | 0.000 | 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) | 0.007 | 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".