ANALISIS PENGARUH PERTUMBUHAN EKONOMI, INFLASI, DAN SUKU BUNGA TERHADAP KREDIT MACET DI INDONESIA
Bibliographic record
Abstract
Abstract This study aimed to analyze the effect of Macroeconomic variables in the form of Economic Growth, Inflation and interest rate of Bank Indonesia (7-Day Repo rate) on Non Performing Loans (NPL) in Indonesia. This study uses annual time series data from 2000 to 2017 with a total sample of 18 years. The model used is Auto Regressive Distributed Lags (ARDL) using Eviews 9. Software The results show that in the short run Inflation has a negative effect on Non Performing Loans (NPL) and Inflation in the previous year (Lag-1) has a significant positive effect whereas in the long run Inflation has a negative effect, maintained inflation at a reasonable limit to foster a good climate for entrepreneurs to be a stimulus so that they are able to fulfill their obligations, in the long run Economic growth has a significant negative effect and interest rates have a significant positive effect. It is hoped that the government can be more careful in setting the 7-Day Repo rate, given the positive response shown to Non Performing Loans (NPL). In addition, the government must also be able to maintain sustainable economic growth given its negative relationship to Non Performing Loans (NPL). It is recommended for further researchers to add other variables such as stock index, exchange rate, Capital Adequacy Ratio (CAR) and Charge-off policy (PH) of non-performing loans.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".