DETERMINING FACTORS IDENTIFYING POVERTY RATE DUE TO COVID-19 PANDEMIC IN NORTH SUMATERA USING THE LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) METHOD
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Published:
Apr 30, 2022
Keywords:
Covid-19, Poverty, Multicollinearity, LASSO, LARS
Section
Computer Science
Statistics Article
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Abstract
Province of North Sumatera is there in ranked 17th with the highest number of poor society in March 2020. Over the last three years, the poverty rate in North Sumatera has decreased in number and percentage. Then there was occur in increase poverty in terms of number and percentage of poor society in the March 2020 to September 2020 period caused by the covid-19 pandemic. This research was conducted to determining factors identifyin poverty due to the Covid-19 pandemic. LASSO is a method that can overcome the problem of multicollinearity by reducing the coefficient of the variable to zero or close to zero at the same time as a variable selection. The computation LASSO using the LARS algorithm. In this research, the factors that identifying poverty due to the Covid-19 pandemic are the open unemployment rate factor, the human development index, the district/city minimum wage and the number of unemployed 15 years and over with a classification accuracy of 98.1%.
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Sari, N. I., Cipta, H. . and Fathoni, M. . (2022) “DETERMINING FACTORS IDENTIFYING POVERTY RATE DUE TO COVID-19 PANDEMIC IN NORTH SUMATERA USING THE LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) METHOD”, Jurnal Mantik, 6(1), pp. 271-277. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/2252 (Accessed: 13May2026).
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[4] Andana.A.P, Safitri.-D, Rusgiyono.-A.”Model-Regresi Menggunakan-Least Absolute-Shrinkage Selection-Operator (LASSO) pada Information Banyaknya Gizi-Buruk Kabupaten/Kota di Jawa.Tengah”. Jurnal-Gaussian,. vol. 6. pp. 21-30. 2017.
[5] Bappenas. Rencana.Strategik Penanggulangan.Kemiskinan di-Indonesia. Jakarta. 2004.
[6] BPS. Profil Kemiskinan Provinsi-Sumatera Utara Maret 2020. Medan: BPS Sumatera Utara. 2020.
[7] Dewi.-Y. S. “OLS,-LASSO dan-PLS Pada Data-Mengandung Multikolinearitas”..Jurnal Ilmu..Dasar. vol.-11. pp. 83-91.
[8] Efron, B. “Least.Angle Regression”. The.Annals of-Statistics. vol. 32. pp. 407-499. 2004.
[9] Hastie..T,-Tibshirani. R,.and Friedman.-J.The.Elements of-Statistical Learning:.Data mining,-Inference and.Prediction.. Springer, New.York. 2008.
[10] Jolliffe, I.T. Principle Component.Analysis Second.Edition. New-York:-Springer-Verlag. 2002.
[11] Sartika, I, Debataraja, N., dan Imro'ah, N. “Analisis.Regresi dengan.Metode Least.Absolute Shrinkage.Selection Operator.(LASSO) dalam.Mengatasi Multikolinearitas”.-Buletin Ilmiah.Math. Stat.-dan Terapannya.(Bimaster). vol..9. pp. 31-38. 2020.
[12] Ghozali,.I..Aplikasi.Analisis Multivariat.dengan Program .IBM SPSS-19. Semarang:.Badan.Penerbit Universitas.Diponegoro. 2011.
[13] Prabowo,-F.K, Rusgiyono,-A, Wilandari..Y. “Pemodelan.Pertumbuhan Ekonomi.Jawa Tengah-Menggunakan Pendekatann-Least Absolute Shrinkage.Selection Operator.(LASSO)”. Jurnal.Gaussian. vol.-4. pp. 855-864. 2015.
[14] Zhao,.P., and .Yu, B. “On.Model Selection.Consistency of.Lasso. Journal.of Machine.Learning.Reaserch”. vol. 7. pp. 2541-2562. 2006.
[15] R. Tibshirani. “Regression Shrinkage and Selection via The LASSO”. Journal Royal Statistic Soc B, vol. 58, pp. 267 – 288. 1996.
[16] A. Mohammad, and H.S. Nghiemb. “Do Instructional Attributes Pose Multicollinearity Problems An Empirical Exploration.” Economic Analysis and Policy, vol. 3, pp. 351–61. 2010.
[17] A. David. “Science Activity and Toxicity Modelling of Some NCI Selected Compounds against Leukemia P388ADR Cell Line Using Genetic Algorithm Multiple Linear Regressions” Journal of King Saud University. 2018.
[18] M. Dyar. “Spectrochimica Acta Part B Comparison of Partial Least Squares and Lasso Regression Techniques as Applied to Laser-Induced Breakdown Spectroscopy of Geological Samples”. Spectrochimica Acta Part B: Atomic Spectroscopy, vol. 70. pp. 51–67. 2012.
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[20] Hastie. T, Efron. B, Johnstone. I, Tibshirani. R. “Least Angle Regression”. Annals of Statistics, vol. 32. pp. 407-499. 2004.
[21] Kutner, M.H., Nachtsheim. C, and Neter. J. Applied Linear Regression Models. 4th ed. New York: Mc GrawHill Companies, Inc. 2017.
[22] Sermpinis. G, Serafeim. T, and Ping. Z. “Modelling Market Implied Ratings Using LASSO Variable Selection Techniques.” Journal of Empirical Finance. vol. 48. pp. 19–35. 2018.
[23] Wang, S. “Predicting Ship Fuel Consumption Based on LASSO Regression.” Transportation Research. 2017.
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