FAKTOR INTERNAL DAN EKSTERNAL YANG MEMPENGARUHI MARGIN DALAM PEMBIAYAAN AKAD MURABAHAH PADA BANK SYARIAH DI INDONESIA
Bibliographic record
Abstract
This study aims to determine the relationship and influence between internal and external factors that affect margins in financing murabaha contracts in Islamic banks in Indonesia. The type of research used in this research is quantitative research. The data used in this research is time series data for the period January 2021-2023.. The population used in this study is the financial report data of Bank Syariah Indonesia Iskandar Muda Kcp. While the sample in this study is the financial reports of Bank Syariah Indonesia Kcp Iskandar Muda from the first quarter of 2021 to the fourth quarter of 2023, namely 9 samples. Sampling in this study using saturated sampling technique. The calculation of the variables was carried out using the SPSS version 23.0 program. In this study, there are two factors that influence murabahah financing at financial institutions, namely internal factors and external factors. Internal factors are factors originating from within the bank itself. In this study several internal factors that influence the amount of murabahah financing are Non Performing Financing (NPF) and the Financing to Deposit Ratio. Apart from internal factors, the amount of murabahah financing is also influenced by external factors. External factors are factors that come from outside the bank such as inflation. So the variables used in this study are dependent variables such as the amount of Murabahah Financing (Y), while the independent variables used in this study are: Non Performing Financing (NPF) (X1), Financing to Deposit Ratio (FDR), (X2) and inflation (Y). The partial and simultaneous regression coefficient test results show that the variables NPF, FDR, and INFLATION have a significant effect on the amount of murabahah financing. This means that the higher the NPF, FDR, and INFLATION, the amount of Murabahah Financing at Bank Syariah Indonesia KCP Iskandar Muda is increasing.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".