Algorithm C4.5 Application of Interest and Talent Data Mining at SMK Negri 1 Bongas

Abstract

The purpose of this study is to classify student majors to simplify and speed up the determination of the selection of majors so that the process resulting from this selection is more accurate and objective. The design method that will be applied to data mining to determine interests and talents is the C4.5 Decision Tree Algorithm Method. The data used in this research are 331 datasets. The data is classified using the C4.5 Decision Tree Algorithm method. The research results show that the classification of interests and talents can be classified to determine student majors using the C4.5 Algorithm. 2) Of the 331 data divided by 80% training data and 20% testing data, the level of accuracy in the C4.5 Decision Tree Algorithm is 89.39%. So, it can be concluded that the accuracy results are lower than previous studies, which produced an accuracy rate of 100%. This means that by analyzing student interest and aptitude data using C4.5, schools can build a classification model that can assist students in choosing a major that suits their interests and talents thereby increasing the accuracy and efficiency of major selection