Analisis Resesi Ekonomi Indonesia Selama Pandemi Covid-19 Menggunakan Regresi Robust MM-Estimator
DOI:
https://doi.org/10.31764/justek.v6i4.20095Keywords:
Economic Recession, Covid-19, Robust Regression, MM-EstimatorAbstract
Abstract:Â This research discusses how to analyze the economic recession in Indonesia during the Covid-19 pandemic in 2020. Pandemic is one of the causes of economic recession due to inflation and drastic economic decline. The pandemic in question is caused by the spread of the Covid-19 virus which is sufficient and easy to infect humans in all age classes. As a result of the recession, the welfare of the community has decreased. In this study, researchers will analyze Indonesia's economic recession during the Covid-19 pandemic using real PRDB data. Real GRDP data was chosen because it will facilitate research because the data has a more specific value to be studied. This research uses quantitative research methods with Robust MM-Estimator regression. Robust regression is one of the methods in regression that serves to find the lowest error data. MM- Estimator is a combination of M- Estimator and S- Estimator so that it is resistant and has greater efficiency. The results of this research conducted using R Studio software show that there are outlier values in Indonesia's real GRDP data in 2020. Identified as outliers are observations 1, 4, 11, 13, 15, and 16.
Abstrak: Penelitian ini bertujuan untuk menganalisis data PDRB riil Indonesia tahun 2020 sebagai indikator resesi ekonomi dan menunjukkan daerah-daerah mana saja yang memiliki penanganan terbaik di sektornya masing-masing. Daerah tersebut akan menjadi acuan bagi daerah-daerah lain untuk mempersiapkan diri bila Indonesia kembali mengalami resesi ekonomi. Penelitian ini menggunakan metode penelitian kuantitatif dengan regresi Robust MM-Estimator. Regresi Robust merupakan salah satu metode didalam regresi yang berfungsi untuk mencari data error terendah. MM- Estimator merupakan penggabungan antara M- Estimator serta S- Estimator sehingga bersifat resisten dan memiliki efisiensi yang lebih besar. Hasil dari penelitian ini dilakukan dengan menggunakan software R Studio menunjukkan bahwa terdapat nilai pencilan pada data PDRB riil Indonesia tahun 2020. Teridentifikasi sebagai pencilan yaitu amatan ke-1, 4, 11, 13, 15, dan 16.References
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