Open AccessMetode Seleksi Fitur Untuk Klasifikasi Sentimen Menggunakan Algoritma Naive Bayes: Sebuah Literature Review
Author(s) -
Fitria Septianingrum,
Agung Susilo Yuda Irawan
Publication year - 2021
Publication title -
jurnal media informatika budidarma/jurnal media informatika budidarma
Language(s) - English
Resource type - Journals
eISSN - 2614-5278
pISSN - 2548-8368
DOI - 10.30865/mib.v5i3.2983
Subject(s) - naive bayes classifier , computer science , feature selection , particle swarm optimization , the internet , artificial intelligence , machine learning , feature (linguistics) , bayes' theorem , selection (genetic algorithm) , data mining , support vector machine , bayesian probability , world wide web , linguistics , philosophy
In the era of the industrial revolution 4.0 as it is today, where the internet is a necessity for people to live their daily lives. The high intensity of internet use in the community, it causes the distribution of information in it to spread widely and quickly. The rapid distribution of information on the internet is also in line with the growing growth of digital data, so that the public opinions contained therein become important things. Because, from this digital data, it can be processed with sentiment analysis in order to obtain useful information about issues that are developing in the community or to find out public opinion on a company's product. The number of studies related to sentiment analysis that applies the Naive Bayes algorithm to solve the problem, so researchers are interested in conducting research on the use of feature selection for the algorithm. Therefore, this research was conducted to determine what feature selection is the most optimal when combined with the Naive Bayes algorithm using the Systematic Literature Review (SLR) research method. The results of this study concluded that the most optimal feature selection method when combined with the Naive Bayes algorithm is the Particle Swarm Optimization (PSO) method with an average accuracy value of 89.08%.