Application of Data Mining to Predicte Student Graduation Rate Using Naive Bayes Method (Case Study of Smp Dharma Wanita 17 Wonoayu)
Penerapan Data Mining Untuk Prediksi Tingkat Kelulusan Siswa Menggunakan Metode Naive Bayes (Studi Kasus Smp Dharma Wanita 17 Wonoayu)
DOI:
https://doi.org/10.21070/ups.1587Keywords:
Data Mining, Naive Bayes, PredictionAbstract
Data mining is a way to find new information extracted from large amounts of data that helps in decision making. By utilizing student main data, student graduation and student average scores as data sources, it is expected to produce information about student graduation rates through data mining techniques. The graduation prediction category is measured from the attributes of gender, report card scores, test scores, etc. This study used a dataset of students from class 2015-2020 at Smp Dharma Wanita 17 Wonoayu, which totaled 450 data. The data is divided into two, namely training data by 70% and data testing by 30%. From testing the data on the rapid miner application, the calculation results obtained an accuracy rate of 99.26%.
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