ANALISIS PENGARUH PERTUMBUHAN EKONOMI, INFLASI, DAN SUKU BUNGA TERHADAP KREDIT MACET DI INDONESIA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it