Analisis Pengaruh Faktor Internal dan Faktor Eksternal Terhadap Non Performing Financing (NPF) Pada Bank Umum Syariah di Indonesia
Bibliographic record
Abstract
This study aims to analyze the influence of internal factors such as FDR, CAR, and BOPO and external factors such as inflation, BI rate, and exchange rates against Non-Performing Financing (NPF) at Islamic Commercial Bank in Indonesia. This study uses a quantitative approach carried out at Islamic Commercial Banks registered at OJK from the first quarter of 2019 to the third quarter of 2021. The sample in this study was determined using a purposive sampling method so that 9 Islamic Commercial Banks were obtained according to the criteria. The data analysis method used is multiple linear regression and a goodness of fit test of a model that is processed using IBM SPSS 25. Based on the results of data testing shows that: (1) The Financing to Deposit Ratio has no significant effect on Non Performing Financing; (2) Capital Adequacy Ratio has no significant effect on Non-Performing Financing; (3) Operating Expense on Operating Income has a positive and significant effect on Non-Performing Financing, (4) Inflation does not have a significant effect on Non-Performing Financing, (5) the BI rate does not significantly affect Non-Performing Financing, and (6) The Exchange rate has a positive and significant effect on Non-Performing Financing
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".