ANALISIS FAKTOR-FAKTOR \nYANG MEMPENGARUHI INFLASI \nDI INDONESIA PERIODE 2000.1 â 2011.4
Bibliographic record
Abstract
One of the problems that often occur in developing countries in implementing the country's development is how to maintain stability and economic growth. Economic stability in terms of stability regarding the price level, the level of national income and employment growth. The main objective of this study was to analyze the factors affecting inflation in Indonesia in 2000.1-2011.4 period. The variables used are: gross domestic product (GDP), the money supply in a broad sense (M2), interest rate, Bank Indonesia certificates (SBI), and the exchange rate of rupiah against the U.S. dollar. \nThe data used in this study is time series data in the quarterly period from 2000.1 to 2011.4, using multiple linear regression with the method of Ordinary Least Square (OLS). \nThe results of this analysis states that the variable gross domestic product and the SBI rate are positive and significant effect on inflation. While exchange rate are positive and not significant effect on inflation. In the other hand, the variables in the money supply (M2) is negative and significant effect on inflation in the quarter a year of research.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".