Implementation of Data Mining Using the Naive Bayes Method to Predict Acceptance of New Students at MTS NU Islamiyah Asembagus
DOI:
https://doi.org/10.33379/gtech.v7i4.3086Keywords:
Data Mining, Naive Bayes, PrediksiAbstract
Acceptance of new students is an important first step in entering the world of education. One of them is at MTs NU Islamiyah, an educational institution in Trigonco Village, Asembagus District. Even though it is able to accept new students every year, fluctuations in the number of incoming students are not a challenge for this institution. This phenomenon influences future policy planning, including the optimal preparation of facilities and infrastructure. This study aims to build a prediction system for new student admissions at MTS NU Islamiyah using the Naive Bayes method using Python. The aim is to improve the prediction accuracy of the number of new students who enter each year. The student data used is 623 training data and 82 test data used as the basis for predictions. The Naive Bayes method is used to classify data by calculating probabilities based on historical data. The prediction results obtained are predictions not included in the 0% precision class and the Support 11 model has a poor performance in predicting the excluded class. As for predictions that are included in the class, the precision is 87% and the support is 71. The model has good performance in predicting the class that is entered.
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