THE CONTRIBUTION OF THE ECONOMIC SECTOR TO INDONESIA'S ECONOMIC GROWTH 2020 TO 2022
Bibliographic record
Abstract
This research on the contribution of the economic sector to Indonesia's economic growth aims to find out an explanation of how the contribution of the economic sector through its various activities has an influence on Indonesia's economic growth as indicated by the size of the GDP, and to examine what types of economic sectors make the biggest contribution to Indonesia's economic growth. Number-based data analysis with quantitative methods, namely by processing and developing data, can be like data on economic growth in percentages and the size of GDP at prevailing prices. In this study, it can be said that the economic sector has a major contribution to economic growth in Indonesia. The economic sector through its various activities can have an impact in the form of an increase in Gross Domestic Product (GDP) and a decrease in GDP which can later be used to determine the occurrence of economic growth in Indonesia. Then from the data that has been taken, it can be seen that there has been a decline in economic growth throughout most of 2020 and the first quarter of 2021. Meanwhile, in the second quarter of 2021 until the first quarter of 2022, the percentage of Indonesia's economic growth continues to show a positive number. The highest percentage of economic growth occurred in the second quarter of 2021, while at the end of 2021 to the beginning of 2022 there was economic growth with numbers that tended to be stable. There is a sector that has an effect on economic growth in Indonesia from 2020, 2021, to the first quarter of 2022, namely the manufacturing industry.
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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 | 0.002 |
| 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) | 0.003 | 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".