The 12-year compulsory education is one of the main programs of the government as part of "Nawacita" which is the mission plan of the Ministry of Education and Culture. One of the parameters of educational success is completing the Gross Participation Rate and the Participation Rate in the pure quality of education to reach 95%. The size of the percentage value of the Gross Participation Rate and the Pure Participation Rate is very closely related to dropping out of school. In this study, analyzing the factors that influence high school dropout students. It is suspected that there is a spatial dependency effect in this case, one way to solve the spatial dependency effect is to use an area approach regression. The regression with the area approach used in this study is the Spatial Autoregressive Model (SAR). There are no spatial drivers, so the linear regression model is more appropriate for modeling. Predictor variables that affect the number of high school dropout students are the variable number of high school and the number of heads of households with the last elementary-junior high school education with a value of R2 = 94%. Both predictor variables are directly proportional to the variable number of students dropping out of school.
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Research Article| April 21 2020
Linear regression model and spatial autoregressive model for modeling high school dropout
AIP Conf. Proc. 2229, 020010 (2020)
Jaka Nugraha, Asriyanti Ali; Linear regression model and spatial autoregressive model for modeling high school dropout. AIP Conf. Proc. 21 April 2020; 2229 (1): 020010. https://doi.org/10.1063/5.0002420
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