Optimization of Decision Tree Algorithm in Text Classification of Job Applicants Using Particle Swarm Optimization

Abstract

Job interview is one of the stages that must be passed by job applicants before getting a job. However, the manual interview process resulted in a large amount of cost and time of selection, so we need a system that can provide recommendations of which applicants are qualified. Currently there are many studies of text classification using the Naive Bayes method, K-Nearest Neighbor, Support Vector Machine, and Deep Learning. Therefore, algorithms that are used less frequently will be tested in this study, such as the Decision Tree. Then the Swarm Intelligence method, Particle Swarm Optimization, is implemented to improve the performance of the method. So that this research focuses on testing and comparing ordinary Decision Tree and Decision Tree methods that are optimized with Particle Swarm Optimization. From the test results, the accuracy of the optimized model increased by 7.1%, and the highest accuracy achieved was 74.3%.