Pertumbuhan Ekonomi Dan Inflasi Di Indonesia Pada Masa Pandemi
Bibliographic record
Abstract
The Covid-19 pandemic has caused recessions in many countries around the world. This happened after economic growth in the first and second quarters of 2020. Some of the countries experiencing recession are Singapore, South Korea, Germany, Japan, France, Hong Kong, and the United States. If the economic growth in each quarter is also negative, Indonesia will experience a recession. The Central Statistics Agency (BPS) noted that Indonesia's economic growth rate fell to minus (5.32%) in the second quarter of 2020. Previously, Indonesia's economic growth in the first quarter of 2020 was 2.97% or started to slow down. Inflation is a tendency to increase the prices of goods and services in general, which continues continuously, which will reduce the purchasing power of the public, especially for low-income groups. Therefore, it is hoped that there will be a control over the rate of inflation, especially during the Covid 19 Pandemic which had an impact on Indonesia's macro conditions. This observation discusses "The Impact of the Covid-19 Pandemic on Indonesia's Inflation Rate", aims to determine the effect of the Covid-19 Pandemic on the Level of Inflation in Indonesia. The results of this observation show that in March 2020 there was inflation of 2.96% year on year (yoy), with an increase in the price of gold jewelery and several food prices that experienced a quite drastic increase.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".