Bibliographic record
Abstract
The Indonesian economy since the first quarter of 2020 has declined. The Covid-19 pandemic has suppressed Indonesia's economic growth. The Ministry of Finance stated that the Indonesian economy in 2020 is estimated to reach minus 1.7 percent to 0.6 percent. The purpose of this study is to determine the prediction of Indonesia's GDP amid Covid-19 pandemic. This type of research is a quantitative study using secondary data with a sample size of 22 samples. The data analysis technique used is the ARIMA method. The results showed stationary data at the second level. Identification of the Bob-Jenkins model selected the ARIMA model (4,2,1). The forecast results show that Indonesia's GDP in the second quarter of 2020 until the second quarter of 2023 will continue to decline. Therefore, policies to promote economic recovery are required. This policy must support the improvement of the health system to reduce the impact of the Covid-19 pandemic on activities and community works. Long-term impacts can be maintained by improving administration, facilitating a more investor-friendly business environment, and increasing budgets to improve education and health facilities.Perekonomian Indonesia sejak triwulan IV-2020 telah mengalami penurunan. Pandemi Covid-19 telah menekan pertumbuhan ekonomi Indonesia. Kementerian Keuangan menyatakan, perekonomian Indonesia pada 2020 diperkirakan mencapai minus 1,7 persen hingga 0,6 persen. Tujuan penelitian ini adalah untuk mengetahui prediksi PDB Indonesia. Jenis penelitian ini adalah kuantitatif dengan menggunakan data sekunder dengan jumlah sampel sebanyak 22 sampel. Teknik analisis data yang digunakan adalah metode ARIMA. Hasil penelitian menunjukkan bahwa data stasioner pada tingkat kedua. Identifikasi model Bob-Jenkins terpilih model ARIMA (4,2,1). Hasil peramalan menunjukkan bahwa PDB Indonesia triwulan II-2020 smpai dengan triwulan II-2023 terus mengalami penurunan. Oleh karena itu, diperlukan kebijakan yang mendorong pemulihan ekonomi. Kebijakan tersebut harus mendukung peningkatan sistem kesehatan untuk mengurangi dampak pandemi Covid-19 pada aktivitas dan pekerjaan masyarakat. Dampak jangka panjang dapat dikurangi dengan perbaikan tata kelola, lingkungan bisnis yang lebih ramah kepada investor dan meningkatkan anggaran untuk memperbaiki fasilitas pendidikan dan kesehatan.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".