DETERMINING FACTORS IDENTIFYING POVERTY RATE DUE TO COVID-19 PANDEMIC IN NORTH SUMATERA USING THE LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) METHOD - Nur Indah Sari; Hendra Cipta; Muhammad Fathoni (Jurnal Mantik, 2022)
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%.