Penerapan Metode Analisis Regresi Logistik Biner Dan Classification And Regression Tree (CART) Pada Faktor yang Mempengaruhi Lama Masa Studi Mahasiswa - Chairunnisa Chairunnisa; Yuki Novia Nasution; Ika Purnamasari (Program Studi Statistika FMIPA Universitas Mulawarman, 2017)
Binary Logistic Regression is one of the logistic regression analysis which is used to analyze the relationship between a dichotomous dependent variable with several independent variables. Classification and Regression Tree (CART) is one of the methods that developed to perform classification analysis on dependent variables either on nominal, ordinal, or continuous scale. In this research, Binary Logistic Regression method and Classification and Regression Tree (CART) applied to the data of the students at Faculty of Math and Natural Science Mulawarman University graduated in year 2016 to determine the characteristic of student which is classified according to two categories that is the study period less than or equal to 5 years and study period more than 5 Years, with five independent variables namely GPA Graduates (X1), Gender (X2), Type of Junior School (X3), Domicile (X4), and Major (X5). Factors that influence the study period of the students based on Binary Logistic Regression method are GPA, Gender, Secondary School Type and Major. The result of classification by using CART method is the student who have the study period less than or equal to 5 Years is a student from Chemistry major or have GPA between 3,51 and 4,00, while the study period more than 5 Year is the student who have GPA between 2,00 and 2,75; 2,76 and 3,50. In terms of classification accuracy, Binary Logistic Regression method was able to accurately predict the observation as much as 75.0%, while the CART method was able to accurately predict the observation as much as 77.27%.