Perubahan Fluktuatif Struktur Ekonomi Indonesia Pada Masa Pandemi Covid-19
Bibliographic record
Abstract
Economic growth in Indonesia has experienced fluctuating changes in 2020, especially entering the Covid-19 pandemic. The subjectivity factor greatly affects Indonesia's economic growth. The important factors that play a role are the low income per person of the population and the large carry-over of the workforce which takes place under pressure from very vulnerable external conditions, which is reflected in the high burden of foreign debt. This is due to being too dependent on imported products which have eroded labor productivity and the empowerment of natural resources has greatly decreased. Economic growth has declined sharply with the spread of the Covid-19 virus which began to enter Indonesia in the second quarter of 2020. The Covid-19 pandemic has greatly affected the movement of the Indonesian economy which tends to decline. This study uses a qualitative method with a descriptive level of explanation, centralized data collection inaccurate social media reports from reliable informant sources, namely the statement of the Minister of Finance Sri Mulyani. The presentation of the research results that in the first quarter (the period from January 2020 to March 2020) economic growth was recorded at a safe rate of plus 2.97%, in the second quarter (April 2020 to June 2020) showed a slump in economic growth, namely minus 5, 32%, there was a significant change, namely a slight increase but still a minus occurred in the third quarter (July 2020 to September 2020) which was minus 1% to minus 2.9%. The negative conditions in the last two quarters resulted in the Indonesian economy falling into a recession. The government must make various important efforts to overcome this recession, namely by deregulating, reducing loan interest rates, increasing interest rates or public savings margins, promoting non-oil and gas exports, expanding job opportunities, restructuring processes due to the still gloomy external situation, increasing taxes, reduction of imported products, handling of the informal sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.011 | 0.003 |
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".