Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Vol. 9, No. 2 August 2018

Implementation Equal-Width Interval Discretization in Naive Bayes Method for Increasing Accuracy of Students' Majors Prediction

Alfa Saleh (Universitas Potensi Utama)
Fina Nasari (Faculty of Computer Science and Engineering, Potensi Utama University)



Article Info

Publish Date
15 Sep 2018

Abstract

The Selection of majors for students is a positive step that is done to focus students in accordance with their potential, it is considered important because with the majors, students are expected to develop academic ability according to the field of interest. In previous research, Naive Bayes method has been tested to classify the student’s department based on the criteria that support the case study on Private Madrasah Aliyah PAB 6 Helvetia students and the accuracy of the test from 100 student data is 90%. in this study, the researcher developed a previously used method by applying an equal-width interval discretization that would transform numerical or continuous criteria into a categorical criteria with a predetermined k value, different k values ??would be tested to find the best accuracy value. from the 120-student data that have been tested, it is proved that the result of the classification of the application of equal-width interval discretization on the Naive Bayes method with the value of k = 8 is better and increased the accuracy value 91.7% to 93.3%.

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Journal Info

Abbrev

lontar

Publisher

Subject

Computer Science & IT

Description

Lontar Komputer [ISSN Print 2088-1541] [ISSN Online 2541-5832] is a journal that focuses on the theory, practice, and methodology of all aspects of technology in the field of computer science and engineering as well as productive and innovative ideas related to new technology and information ...