Open AccessNaive Bayes Method for Classification of Student Interest Based on Website Accessed
Author(s) -
Alwis Nazir,
Amany Akhyar,
Muhammad Ramadhani,
. Herlina
Publication year - 2020
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 - 10.1088/1742-6596/1655/1/012104
Subject(s) - naive bayes classifier , computer science , confusion matrix , the internet , class (philosophy) , world wide web , confusion , feeling , web page , social media , artificial intelligence , psychology , social psychology , support vector machine , psychoanalysis
Interest is a feeling of liking a thing or activity without any coercion. Students' interest in a certain subject will maintain students' learning abilities, thus they could master it and get good learning outcomes. Interest can be known from the website accessed. The aim of this study is to build a web-based application that could classify student interest using Naïve Bayes based on the website accessed. In this study, the data used are 17.265 student internet history data. The application was tested using Black Box and the method was tested using Confusion Matrix. The result from the application testing met the expectation, and the method (Naïve Bayes) reached 99,81% accuracy using 70:30 data percentage. The top five classes obtained are Social Networking, Educational Institution, Streaming Video, Search Engines, and Web-Based Mail. The “Develop” class was also found, thus the study group related to application development is recommended to be formed.