PREDICTION OF CUMULATIVE ACHIEVEMENT INDEX (IPK) USING THE LINEAR REGRESSION METHOD - Santoso, Imam; Nursiaga, Rahmat (Journal of Information System, Applied, Management, Accounting and Research, 2026)
Higher education as a business institution engaged in educational services cannot be separated from the reach of globalization. Changes in educational trends and the free movement of science and technology which are important aspects of globalization will affect the field of education. The final measurement of lectures is the achievement of the GPA as an achievement of the lecture process. To find out the predicted GPA value before the final semester, it can be done using the Multiple Linear Regression (MLR) algorithm using the Interim Grade Point Average (IPS) independent variable for semesters 1 and 5. With the prediction obtained at the end of semester 5, it is hoped that it can motivate students to improve their grades in the following semester. With a dataset from graduates of the Muhammadiyah University of Technology Jakarta (UTM Jakarta), a prediction model was built with the Python programming language, resulting in a moderate MLR model with an MSE evaluation value of 0.016 and R2 of 0.59 with the interpretation of a Good Model and will be better if other independent variables are added.