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Record W4292692863 · doi:10.21154/elbarka.v4i1.3016

Forecasting of Indonesia's Gross Domestic Product Amid Covid-19 Pandemic

2021· article· en· W4292692863 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEl-Barka Journal of Islamic Economics and Business · 2021
Typearticle
Languageen
FieldComputer Science
TopicData Mining and Machine Learning Applications
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Autoregressive integrated moving averageChristian ministryCoronavirus disease 2019 (COVID-19)PandemicGross domestic productGeographyIndonesianBusinessEconomic growthEconomicsPolitical scienceStatisticsMathematicsTime seriesMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.281
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it