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All Journal Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences
Mia Miskiatul Atiroh
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Religion Arts Humanities Economics, Econometrics & Finance Social Sciences

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Classification of the Nave Bayes Method in Determining the Concentration of Information Programs Mia Miskiatul Atiroh
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 5, No 2 (2022): Budapest International Research and Critics Institute May
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v5i2.5655

Abstract

The Faculty of Information Technology is one of the faculties at Serang Raya University which has three study programs, namely Information Systems (IS), Computer Engineering and Informatics. In the informatics study program there are two concentrations, namely programming and multimedia concentrations. Determination of concentration cannot be separated from student supervision, on the other hand, academics must have their own policies in determining student concentration. The method used in this research is Nave Bayes which has 10 variables, namely Algorithm, Calculus I, Internet Html, Commerce Package Program, Graphic Design, Hardware & Software, Calculus II, Data Communication, Introduction to Object Oriented I, Introduction to Information Technology. In this study, there were 248 student data from the informatics study program where 100 student data consisted of students who had taken concentration and 148 data were students who had not taken concentration. In the calculation of 100 student data who have taken concentration, it is known that the number of data with programming concentration is 87 students while for multimedia concentration there are 13 students. Furthermore, the 100 data is divided into 80 training data and 20 testing data using random sampling technique. Based on student academic data which was used as testing data, the Naive Bayes method was successful in classifying 20 student data from 100 student data. Thus, the Naive Bayes method is successful in classifying concentrations with an accuracy success rate of 0.80 (80%) and an accuracy of 0.565 Kappa statistics so that the concentration selection using the Nave Bayes classifier method is accurate.
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