Dampak Covid-19 Terhadap Perekonomian Indonesia Dari Sisi Pendapatan Nasional Pendekatan Pengeluaran
Bibliographic record
Abstract
This 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 expenditure method with components of household consumption, gross investment, expenditure and net exports, and future predictions, if the Covid-19 pandemic will continue in the future. long time. From the expenditure side, economic growth in quarter II-2020 compared to quarter II-2019 (y-on-y) contracted in all components. The deepest contraction occurred in the Export of Goods and Services Component of 11.66 percent, followed by the Gross Fixed Capital Formation Component with a contraction of 8.61 percent. The growth in the component of the LNPRT Consumption Expenditure contracted by 7.76 percent, and the growth in the Government Consumption Expenditure component contracted by 6.90 percent. When compared with the previous quarter (q-to-q), economic growth from the expenditure side contracted in all components except for the Government Consumption Component, which grew by 22.32 percent. This is due to an increase in spending on social assistance, especially for the response to the Covid-19 pandemic. The component that experienced the deepest contraction occurred in exports of goods and services amounting to 12.81 percent. Meanwhile, imports of goods and services as a subtracting component decreased by 14.16 percent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".