Perbandingan Metode C45 dan Naive Baiyes untuk Sistem Prediksi Pemilihan Jurusan di SMK Muhammadiyah 10 Kisaran

Abstract

This research is motivated by the large number of prospective students who simply choose a major when they want to enter a vocational school without considering their abilities. The Decision Tree or C45 method is used because it is able to make decision trees that are easy to describe, and has a level of efficiency in handling discrete and numeric attribute data. While the Naive Bayes method is used because it has a high accuracy of results. This research was conducted based on data from students of SMK Muhammadiyah 10 Kisaran which contained questions about feelings of wrong majors, interests, and determinants of other majors. Data is divided into 2 labels, namely free labels (y) and bound labels (x). Followed by dividing the dataset into training data and testing data with a ratio of 70:30 in both methods to get the level of accuracy. From the results given, it can be seen that the C45 algorithm has an accuracy of 85% and the Naive Bayes algorithm has an accuracy of 26%. This shows that the C45 algorithm is more effective in classifying the available datasets compared to the Naive Bayes.