Classification Techniques in Predicting New Student Admission Using the Naïve Bayes Method
DOI:
https://doi.org/10.55123/jomlai.v1i3.963Keywords:
Classification, Data Mining, Naive Bayes, Prediction, New StudentAbstract
Admission of new students is the registration process for new students entering school and the initial gate through which students enter the object of Education; this activity is the starting point for determining the smoothness of the tasks of a school, assisted by teaching staff and equipped with optimal facilities and infrastructure in teaching and learning activities, producing skilled and broad-minded students. However, the uncertainty of the number of registrants also influences the policies that will be taken in the future. Therefore, it is necessary to forecast or predict to estimate the number of students who are likely to register so that the school can prepare everything. In this study, the prediction process for new students will use a classification technique using the Naïve Bayes method. This study aims to predict the rise and fall of the number of students who register using the Naïve Bayes method. The research data was obtained by distributing questionnaires randomly to 200 respondents (students) who were about to enter high school. The data is accumulated using the help of Microsoft excel. The results obtained are that the prediction of high-class precision is 100%, while the prediction of low-class precision is 94.23%. The conclusion is that the extracurricular, cost and distance criteria need attention and improvement. This is because disinterest and low prediction are higher than interest with high prediction results.
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