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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Muhammad Arfah Wahlil
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Computer Science & IT

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Implementasi Klasifikasi Naive Bayes Dalam Memprediksi Lama Studi Mahasiswa Muhammad Fuad; Muhammad Arfah Wahlil; Hazriani, Hazriani; Yuyun, Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Under normal conditions, undergraduate or undergraduate students from a university can complete their studies for 4 years or 8 semesters. In fact, many students complete their study period of more than 4 years. It is known that in the academic year 20XX/20XX there were 161 people who were accepted as students. Of the 161 people admitted, 100 people have completed their study period of about 4 years and the remaining 61 people have completed their studies for 5 years. Based on the problems above, this research implements a classification that can help the university predict the length of study of students who are currently studying in various study programs at the University. The method that the author presents in the classification for predicting the length of a student's study period is the Naive Bayes Algorithm. By using the Java-based Rapid Miner tool to classify graduation data. Then the implementation of data mining which is divided into 136 data training data and 25 data testing data with naive Bayes managed to obtain an accuracy rate of 82% which is also a relatively good parameter.
Co-Authors Hazriani, Hazriani MUHAMMAD FUAD Yuyun, Yuyun