Factors affecting the grade point average students of FMIPA Universitas Negeri Padang with binary logistic regression model

Abstract

Today, everyone places a high importance on their education. Learning is how education is implemented, and learning allows people to reach their full potential. Since learning is a process and learning achievement is the end consequence of the learning process, learning and learning achievement are inextricably linked. Learning achievement levels are assessed using GPA (Grade Point Average). Allowance, gender, major, status of residence, school location, study time, admission type, duration of gadget use, and personality type are all factors that affect GPA. In order to identify the components that influence academic accomplishment, a model must be developed since it can be understood, explained, controlled, and forecasted. This study's goal is to identify the binary logistic regression model, which describes the variables influencing the faculty of mathematics and natural sciences at Universitas Negeri Padang's GPA. The aim of this study is to identify the logistic regression model that represents the variables that affect the GPA of the Faculty of Mathematics and Natural Sciences at Universitas Negeri Padang. Secondary and primary data were employed in this study, and questionnaires were used to collect the data. The 2020 students made up the study's sample, which included 293 respondents. According to the study's findings, factors such as gender, major, admission type, and duration of gadgets use may have an impact on students' GPAs at the Faculty of Mathematics and Natural Sciences at Universitas Negeri Padang. The percentage of correct predictions between the logistic regression model and training data is 70%.

Keywords

• binary logistic regression
• factors affecting
• grade point average
• prediction model