DAMPAK COVID-19 TERHADAP PEREKONOMIAN INDONESIA DARI SISI PENDAPATAN NASIONAL PENDEKATAN PRODUKSI
Bibliographic record
Abstract
The number of Covid-19 cases worldwide continues to show a rapid increase. As of October 31, 2020, the number of cases has reached 45,866,380 and 1,192,684 deaths worldwide, 410,088 people exposed and 13,869 people died in Indonesia. The study aims to determine the extent of the impact of Covid-19 on the Indonesian economy in terms of national income, which is calculated based on the mode of production (business field) Y = (Pi.Qi)), and predictions in the future, if possible. The Covid-19 pandemic is still going on for a long time. Large-Scale Social Restrictions (PSBB) which were implemented in various regions in Indonesia in April and May, suppressed economic activity in all sectors. Some business sectors have been forced to lay off their employees. Meanwhile, people hold their consumption until conditions are more stable. As a result, Indonesia's economic growth in the second quarter of 2020 contracted by 5.3 percent (YoY). 
 Contribution of corrections came from mining and quarrying (-2.72%), processing industry (6.19%), electricity and gas procurement (-5.46), construction (-5.39%), Wholesale and Retail Trade; Repair of Cars and Motorcycles (-7.57%), Transportation and Warehousing (-30.84%), Provision of Accommodation and Food and Drink (-22.02), company services (-12.09%), and other services ( -12.60%). Then other sectors grew positively in the range of 1.03% -10.88%, with the largest positive growth contribution in the information and communication sector by 10.88%. 
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".