The Exploring of Student’s School Majoring Data at Madrasah Aliyah Negeri in Samarinda Using Linear Discriminant Analysis Models

Abstract

Curriculum 2013, which applied to Madrasah Aliyah Negeri (MAN) requires that majors are implemented since the 10th grade The majors is supposed to relate to the study plan when they study at University. The aims of this research are to describe correspondence between current majors and desired future majors, to observe distribution pattern of student exam data based on their boxplot, to classify student majors data using the Linear Discriminant Analysis (LDA) models, and to get the best LDA model to describe the characteristics of majors in MAN. The LDA models used in this research were Fisher’s Linear Discriminant Analysis (FLDA), Diagonal Linear Discriminant Analysis (DLDA), Shrunken Linear Discriminant Analysis (SLDA), Maximum-uncertainty Linear Discriminant Analysis (MLDA), and Factor-model Linear Discriminant Analysis (RFLDA). The best LDA model was chosen based on the classification accuracy produced by resampling with n = 1000 and n = 5000 for training data and testing data with the probability of each data being drawn were 60:40, 70:30, 80:20, and 90:10.