Open AccessAcademic achievement analysis of Universitas Negeri Semarang students using the naïve bayes classifier algorithm
Author(s) -
Aji Purwinarko,
Wahyu Hardyanto,
Nindya Aryani
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1918/4/042130
Subject(s) - naive bayes classifier , graduation (instrument) , computer science , mathematics education , bayes' theorem , classifier (uml) , machine learning , artificial intelligence , algorithm , psychology , mathematics , bayesian probability , geometry , support vector machine
This study aims to evaluate the academic data of students in third year and classified in the category of students who can graduate on time or not. This system uses students master data and student academic data as input data. Data of students who have graduated will be used as training and testing data while the data of students who have passed will be used as target data. Input data will be processed using the Naïve Bayes Classifier (NBC) data mining algorithm to form a probability table as the basis for the student graduation classification process. The output of this system is in the form of a classification of student academic performance that is predicted to pass and provides recommendations for the graduation process on time or graduates in the most appropriate time with optimal grades.