Data Mining Predicts The Graduation of Students of STMIK Atma Luhur Information System Using Neive Bayes Algorithm

Agus Dendi Rachmatsyah (STMIK Atma Luhur)
Benny Wijaya (STMIK Atma Luhur)
Kiswanto (STMIK Atma Luhur)

Jurnal Mantik
Vol. 4 No. 3 (2020): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)

In studying in higher education, the accuracy of graduation at the end of the semester is important, as is the case for the final semester students at STMIK Atma Luhur Pangkalpinang. STMIK Atma Luhur Pangkalpinang has three Study Programs, one of which is the Information Systems Study Program which receives less than 150 to 200 students each year and is expected to graduate on time. With the number of students who enter, it might not match those students who have completed their studies on time. In this study, the classification of the graduation punctuality was performed using the Naif Bayes algorithm. Neive Bayes predicts future opportunities based on past experience known as Bayes Theorem. The implementation in this study uses the RapidMiner 7.0.001 software. To help find accurate values, the attributes used in this study are NIM, Name, Level, Study Program, Province of Origin, Gender, SKS, GPA, and Year of Graduation. STMIK Atma Luhur.