Pengaruh Tingkat Suku Bunga, Tingkat Bagi Hasil, Likuiditas, Inflasi, Ukuran Bank, dan Pertumbuhan Produk Domestik Bruto terhadap Deposito Mudharabah Bank Umum Syariah di Indonesia
Bibliographic record
Abstract
The purpose of this study is to examine the influence of interest rates, the level of profit sharing, liquidity, inflation, the size of the company, and the growth of gross domestic product simultaneously and partially on mudaraba deposits at Islamic Commercial Banking in Indonesia. This study conduct quantitative research with hypothesis testing on secondary data in term of time series on the quartely financial statements starting from the first quarter of 2011 to fourth quarter of 2014. The reserach sample is six Islamic Commercial Banking in Indonesia. The data analysis technique in this study are descriptive analysis, classical assumption test, multiple regression analysis, hypothesis test uses F test, and t test. The result showed that variable of interest rates, the level of profit sharing, liquidity, inflation, the size of the company, and the growth of gross domestic product simultaneously significant influence on mudaraba deposits at Islamic Commercial Banking in Indonesia. while partially variable of the level of profit sharing, and the size of the company positivelly significant influence on mudaraba deposits at Islamic Commercial Banking in Indonesia, but interest rates, liquidity, inflation, and the growth of gross domestic product does not significant influence on mudaraba deposits at Islamic Commercial Banking in Indonesia.
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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 | 0.002 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".