Open AccessPenerapan Algoritma Naive Bayes Untuk Mengklasifikasi Pengaruh Negatif Game Online Bagi Remaja Milenial
Author(s) -
Nungky Asmiati,
. Fatmawati
Publication year - 2020
Publication title -
jtim : jurnal teknologi informasi dan multimedia/jurnal teknologi informasi dan multimedia
Language(s) - English
Resource type - Journals
eISSN - 2715-2529
pISSN - 2684-9151
DOI - 10.35746/jtim.v2i3.102
Subject(s) - naive bayes classifier , class (philosophy) , smartphone addiction , recall , computer science , addiction , psychology , artificial intelligence , cognitive psychology , support vector machine , neuroscience
In this modern era, the use of electronics such as cellphones, computers, laptops and others quite widely used for various needs. Information technology that is very developed today brings changes and affects social life. One of the problems in society is the large influence of online games because online games themselves have an attraction that makes people more fun playing than learning. It evidenced by the large number of millennial adolescents spending their daily time in front of computers or smartphones instead of books, and the lack of socializing is also one of the negative effects of playing online games and harms their health. To solve these problems, the classification method used is the naïve Bayes algorithm method, for classification in the form of online game user data as a whole, namely based on name, gender, age, number of days, duration and classification in the form of addiction and not addiction (normal). Therefore, this naïve Bayes algorithm can predict future opportunities based on past experiences. The results of the study of 100 online game user data in normal conditions were 78 respondents, and addiction was 22 respondents from the results of both concluded that the research results of millennial adolescents online game users were declared normal with an overall accuracy of 89.00%. Addicted recall class 77.27%, normal recall class 92.31%, addicted precision class 73.91%, normal precision class 93.51%.