Modeling Grade Point Average (GPA) of Students in IAIN Sultan Amai Gorontalousing Binary Logistic Regression Approach - Syilfi; Indriani; Muhammad Obie (Jurusan Matematika FMIPA UNM, 2025)
The objective of this study is to construct a model for the Grade Point Average (GPA) of the 2021 cohort of students enrolled in the Faculty of Education and Teacher Training (FITK) IAIN Sultan Amai Gorontalo, and analyze the factors that influence it. GPA modeling uses the binary logistic regression method. Binary logistic regression is a data analysis technique used to test whether or not there is a correlation between the response variable (y) which has two categories or is binary and the predictor variable (x) which has several categories or is polychotomous. This study uses primary data obtained through a questionnaire with a sample size of 190 students from six majors. The response variable in this study is student GPA and there are 8 independent variables studied. Based on the data obtained, the average GPA of 2021 students is 3.13. The results of data processing show that the variables of region of origin (x1), the number of organizations joined (x3), the average length of study per day (x4), and the average length of internet usage per day (x5) significantly affect the GPA of FITK students. The logit model obtained is . The probability function for obtaining a GPA ≥ 3.13 is .