A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN

Sugiyarto Sugiyarto; Joko Eliyanto; Nursyiva Irsalinda; Zhurwahayati Putri; Meita Fitrianawat

doi:10.31764/jtam.v5i1.3766

A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN

Sugiyarto Sugiyarto, Joko Eliyanto, Nursyiva Irsalinda, Zhurwahayati Putri, Meita Fitrianawat

Abstract

Sentiment analysis is an analysis with an objective to identify like, dislike, comments, opinion, or feedback on certain content which will be categorized into positive, negative, or neutral. In general selection, sentiment analysis widely known to be used to predict the winner on election process. This method tries to dig the people sentiment on their governor candidates during election, whether it’s positive, negative, or neutral opinion. The output of the positive sentiment is related to people acceptance towards one of the election nominee. That statement usually applied as a base reference for determining the result of the election process. In sentiment analysis, the importance of its fuzzy logics must be considered. Each of the people statement is assumed to have the level of positive, negative, or neutral percentage. The concept of fuzzy logic is developed and applied on one of this text mining method. This research is focusing on comparison analysis and fuzzy logic application in sentiment analysis method. Two method which discussed in this research are Fuzzy Naïve Bayes and Sentiment Fuzzy with convolutional neural network. This research is applied on PILKADA of Solo and Medan district case study. The data of the people opinion are acquired from twitter and collected on September 2020 to December 2020. The two methods which mentioned before are implemented on the acquired data and the output of these method application then compared. The conclusion of this research suggest that different approach will resulting in different output.

Keywords

Sentiment Analysis; Fuzzy Logic; Naïve Bayes; Convolutional Neural Network;

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DOI: https://doi.org/10.31764/jtam.v5i1.3766