Analysis of IT Graduates Employment Alignment Using C4.5 and Naïve Bayes Algorithm

Abstract

© 2020, World Academy of Research in Science and Engineering. All rights reserved. There is a significant increase in the number of graduates produced by higher education institutions every year and the number of graduates is growing immensely compared to job opportunities in the market. Data mining techniques serve as the foundation of various researches in generating knowledge from the dataset obtained from schools, universities, and companies. In this paper, the application of classification mining with the use of the C4.5 and Naïve Bayes algorithms, analyzed the dataset gathered from the Office of the Registrar of Davao del Norte State College. This study aims to determine the correlation between students' grades in their major subjects and their employment right after completing a degree in Information Technology. The experimental results showed that the Naive Bayes algorithm garnered 67.22% accuracy using 10-fold cross-validation scheme and 55.91% accuracy with the 70% training and 30% testing percentage split. Further, the C4.5 algorithm accumulated 95.52% and 92.13% accuracies using the same cross-validation method and data composition percentage split, respectively. It goes to show that the best algorithm in analyzing the employment alignment of the IT students is the C4.5. This research would help the students and the university to improve various aspects of educating the students and see to it that they would produce quality graduates in the following years. The use other algorithms to generate more significant models, as well as adding more attributes to identify other factors affecting graduate’s employment is recommended.