Analisis Pertumbuhan Ekonomi Jawa Timur Pada Masa Covid-19
Bibliographic record
Abstract
This study aims to analyze economic growth during the Covid-19 period in East Java for the 2019-2021 period. This study used a qualitative method using a library research approach. Based on the results of the study, economic growth during the Covid-19 period in East Java for the 2019-2021 period experienced a drastic decline. Where in quarter IV-2020 against quarter IV-2019 (y-on-y) East Java's economy contracted by 2.64 percent. In the first quarter of 2020, growth began to slow down to 2.97 percent. Furthermore, in the second quarter of 2020 it contracted to 5.98 percent compared to the second quarter of 2019. And in the second quarter of 2021 economic conditions began to improve even though they were still contracting, namely by 7.05 percent. Penelitian ini bertujuan untuk menganalisis pertumbuhan ekonomi pada masa Covid-19 di Jawa Timur periode 2019-2021. Penelitian ini menggunakan metode kualitatif dengan menggunakan pendekatan studi Pustaka (Library Research). Bedasarkan hasil penelitian menunjukkan pertumbuhan ekonomi pada masa Covid-19 di Jawa Timur periode 2019-2021 mengalami penurunan secara drastis. Dimana pada triwulan IV-2020 terhadap triwulan IV-2019 (y-on-y) ekonomi Jawa Timur terkontraksi sebesar 2,64 persen. Pada triwulan I-2020, pertumbuhan mulai melambat menjadi 2,97 persen. Selanjutnya triwulan II-2020 terkontraksi mencapai 5,98 persen dibanding triwulan II-2019. Dan pada Triwulan II-2021 kondisi ekonomi mulai membaik walaupun masih terkontraksi, yaitu sebesar 7,05 persen.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".