New normal policy on the Rupiah exchange rate using Long Short-Term Memory
Bibliographic record
Abstract
Abstract Since the world is facing health problems due to COVID-19, Indonesia was not success to surge the outbreak. In addition to the risk of health problems, this pandemic is also disrupting the global economy. The rupiah exchange rate also saw a devaluation during the COVID 19 outbreak. The Indonesian economy is expected to grow negatively in the third quarter of 2020 and predicted to continue until the end of the year. The measure from the government impacted the economic circumstances then along with the decline in new cases of the spread of COVID-19 the government was implementing a new normal, after the completion of large scale social restrictions weakened the economic development, with one of its goals to improve and try to save Indonesia’s economy from a possible worse recession. This study attempts to use the forecasting method to find out whether the application of the new normal will strengthen the Rupiah exchange rate in the coming period. The methods that will be used is Long Short Term Memory as the best model to overcome long-term dependencies to obtain a predictive model that most closely approximates existing data patterns. The most suitable model is Long Short Term Memory with 50 epochs using five hidden neurons. Based on the results, it seems that the Rupiah exchange rate tends to weaken in the next five-day.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".