Optimization of the C4.5 Algorithm Using Particle Swarm Optimization and Discretization in Predicting the Results of English Premier League Football Matches

Abstract

Football is one of the most popular sports. One of the most competitive football competitions is the English Premier League. This study aims to determine the prediction of the results of the football match in English Premier League. The prediction results in the form of home win, away win, and draw. This prediction uses data mining techniques, namely using the C4.5 algorithm as a classification algorithm with Particle Swarm Optimization as a feature selection method and Discretization as a preprocessing method. The dataset used was obtained from the football-data.co.uk website for four league seasons from the 2017/2018 season to the 2020/2021 season with a total of 1,520 instances. In this study, a comparison was made to the methods used to determine the increase in accuracy obtained. Based on ten times the data mining process, the final result of the best accuracy from using the C4.5 algorithm is 57.24%, then the C4.5 algorithm with Discretization gets an accuracy of 65.13%, and the C4.5 algorithm with Discretization and Particle Swarm Optimization gets accuracy of 71.05%. The conclusion is that the use of Discretization and Particle Swarm Optimization can improve the performance of the C4.5 algorithm in predicting the results of English Premier League matches with an increase in accuracy of 13.81%.