LOGISTIC REGRESSION MODEL FOR PREDICTING FIRST SEMESTER STUDENTS GPA CATEGORY BASED ON HIGH SCHOOL ACADEMIC ACHIEVEMENT
Keywords:
logistic regression, prediction, accuracy model, Crosstab tableAbstract
Faculty of Information Technology, Duta Wacana Christian University (DWCU) has two methods of admitting new college students in which the first is on the basis of educational achievements in high school whereas the second is on the basis of regular test entrance examination. This research will seek forms of functional relationships through logistic regression to the first semester GPA category of the student in Faculty of Information Technology, Duta Wacana Christian University. The first semester GPA category is used as the dependent variable and the location of high school, high school class, high school status, and level of English test result are used as independent variables. With regards to the training data required to create a logistic regression model, we used students’ admision data from 2008 through 2014, while the students’ data of 2015 is taken as the testing data. The accuracy of the model in predicting data, is measured by the percentage of correct predictions in this regard through Crosstab tables between the predicted data and the real observation of the 1st semester GPA from new students of 2015 class. This research found seven models. The highest percentage of correct predictions between the logistic regression model and training data is 79.4% .There is a change in the logistic regression form, three models influence only by level of English test result while the remaining four models are influenced by the level of English test result and high school location.
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