Ketahanan Ekonomi Nasional Masa dan Pasca Covid-19 Melalui Penguatan UMKM Indonesia
Bibliographic record
Abstract
The Coronavirus disease-19 (Covid-19) crisis has an impact on the economic performance of Micro, Small and Medium Enterprises (MSMEs) at local and national levels. MSME actors are trying to survive the crisis through business efficiency and the national economic rescue program (PEN). This paper aims to (1) examine the resilience of MSMEs against a wave of crisis (2) the right policy formula to strengthen the competitiveness of MSMEs. This research uses descriptive qualitative method using secondary data. The data is sourced from the Ministry of Small and Medium Enterprises Cooperatives, Bank Indonesia (BI), the Central Statistics Agency (BPS), the National Planning and Development Agency (Bappenas), national and international scientific journals and media sources. Slowly, the condition of MSMEs began to rise from the Covid-19 crisis through the PEN program policy which ran in September-December 2020, economic growth was better in the fourth quarter of 2020 by -2.19 percent (yoy), better than the growth in the third quarter 2020 by -3.49 percent (yoy). The distribution of the PEN program for MSMEs was successful and effective in maintaining the resilience of MSMEs from the Covid-19 crisis. The government needs to maintain economic momentum, through the synergy of MSMEs with State-Owned Enterprises (BUMN), digitalization and the MSME global supply chain as well as increasing the PEN budget for MSMEs. On the other hand, the government still has a tough task to cut logistics costs which are still relatively high when compared to several ASEAN countries.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".