The week's pick
Reseach Article
Comparison of K-Nearest Neighbour Method with Naive Bayes Classifier and Support Vector Machines for Student Graduation Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 9 |
| Year of Publication: 2024 |
| Authors: Rubangi, Sutarman |
10.5120/ijca2024923436
|
Rubangi, Sutarman . Comparison of K-Nearest Neighbour Method with Naive Bayes Classifier and Support Vector Machines for Student Graduation Classification. International Journal of Computer Applications. 186, 9 ( Feb 2024), 14-18. DOI=10.5120/ijca2024923436
Abstract
Timely student graduation is a hallmark of student success in obtaining a bachelor's degree. During the lecture period, students are not necessarily able to complete the lecture period on time because many factors influence student graduation to be late. One of the factors that determine the quality of higher education in lectures is the presentation of students' ability to complete college studies on time. The length of time students study affects the quality of the study programme because student study time is used as one of the criteria in determining the assessment by BAN PT (National Accreditation Board of Higher Education). With the existence of these problems that occur, it can be overcome in research, namely the classification of student graduation on time by using the K-Nearest Neighbor Algorithm, Naive Bayes Classifier and Support Vector Machines algorithm methods to classify the accuracy of student graduation. The implementation of the three algorithms was carried out with Rapid miner software. After training and testing with 543 datasets, the classification of student graduation on time with the best accuracy of the three methods is the K-Nearest Neighbor method, the accuracy obtained is 100%, then for the classification of on-time graduation, the Naive Bayes Classifier method obtained an accuracy of 97.11%, and the Support Vector Machines method of classifying student graduation on time, the accuracy obtained is 84.56%.
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