Analisis Regresi Logistik Biner Terhadap Data Indeks Kedalaman Kemiskinan Di Indonesia Tahun 2020 - Regita Elza Fitri; Eri Setiawan; Mustofa Usman; Dorrah Aziz (Jurnal Siger Matematika, 2022)
Poverty is a serious problem that occurs in many countries, both developing and developed countries. This issue needs to be addressed by the government, especially in countries with large and dense populations such as Indonesia. Poverty inequality as measured by the poverty depth index shows a number that tends to be stable from year to year. Therefore, it’s necessary know the causal factors that affect the depth of poverty in Indonesia. This study discusses the factors that affect the poverty depth index in Indonesia in 2020 using binary logistic regression analysis to determine the best binary logistic regression model and find out the magnitude of the classification accuracy of what factors affect the poverty depth index in 34 provinces in Indonesia in 2020. This problem can be overcome by using binary logistic regression because the response variable only consists of two categories, namely high and low poverty depth. Based on the analysis, it can be concluded that the open unemployment rate variable and the average expenditure per capita for one month for food have a significant effect on the classification of the poverty depth index in Indonesia 2020.