Classification Of Direction Using Naive Bayes Classifier Method (Case Study Of Hidayatul Islam Leces Vocational School)

Abstract

Abstract— Determining student majors is an important process in the world of education that can affect students' future. In this thesis, we conducted a study on determining student majors using the Naive Bayes Classifier algorithm at SMK Hidayatul Islam. The purpose of this study was to test the accuracy of the Naive Bayes Classifier algorithm in predicting student majors and to provide recommendations that can support decision making in determining student majors. This study uses historical data of SMK Hidayatul Islam students which includes various attributes such as academic grades, Mathematics, Science, Language, Science, and Average report card grades. The data was processed and trained on the Naive Bayes Classifier algorithm using machine learning methods. Furthermore, the algorithm was tested using separate test data. The results showed that the Naive Bayes Classifier algorithm provided an accuracy of 97.50% in determining student majors at SMK Hidayatul Islam. This shows a very good ability to predict student majors based on existing attributes. With high accuracy, this algorithm can be an effective tool in helping the student major decision-making process. However, it should be noted that the results of this study need to be considered in the specific context of SMK Hidayatul Islam and the characteristics of its students. Factors such as students' interests and talents, parents' views, and job market needs should also be important considerations in determining students' majors. Therefore, the Naive Bayes Classifier algorithm should be used as one component in a broader decision-making process, which involves consideration of these various factors.