Application of Data Mining for Prediction of High School Student Graduation Rates

Abstract

The implementation of Data Mining in the education sector aims to develop methods that are able to discover valuable knowledge from data generated in the educational environment. This can be used to increase learning efficiency by paying more attention to students who are predicted to have low grades. However, in its application, each algorithm shows different performance depending on the attributes and dataset used. In this study, a dataset of semester grades and final school exam scores was used. Some of the prediction techniques used are decision trees, support vector machines, and neural networks. Of the four scenarios for the science major at SMAN 2 and SMAN 3 Pangkalpinang with 3 different models, the Mean Squared Error value shows that the test results are in accordance with the testing dataset and can be used as predictions of students' final grades, namely the decision tree model and support vector machine. For the Social Sciences major at SMAN 2 and SMAN 3 Pangkalpinang with 3 different models, the Mean Squared Error value shows that the test results are in accordance with the testing dataset and can be used as a prediction of students' final grades, namely the support vector machine model.