ANALISIS PREDIKSI KELULUSAN MAHASISWAFILKOM UNIVERSITAS NURDIN HAMZAH DENGAN METODE NAIVE BAYES

Authors

Analysis, Data Mining, Naive Bayes, Prediction Graduation, Rappidminer

Universities have an obligation to produce competent graduates. This can be measured from the

graduation rate of students. The purpose of the analysis carried out is to predict graduation on time and find out

indicators of problems that cause graduation not to be on time. Many factors influence students' late graduation,

such as the student's marital status, the status of the student working or not working, the level of student

understanding of the course material which can be seen from the student's GPA. Based on the existing problems,

it is necessary to have a system to predict student graduation rates based on existing variables. Attributes used

are Name, Name, Gender, School of Origin, Study Program, Year of College Entry, Year of Graduation, Student

Status, Marital Status, Age, IPS, GPA and Parent's Occupation. The method used is the Naive Bayes method.

Comparison results were carried out using Excel and Rapidminer tools. The resulting accuracy level is 100%

from 330 training data and 98 test data