Perbandingan Seleksi Fitur Term Frequency & Tri-Gram Character Menggunakan Algoritma Naïve Bayes Classifier (Nbc) Pada Tweet Hashtag #2019gantipresiden

Abstract

Towards an election year (elections) in 2019 to come, many mass campaign conducted through social media networks one of them on twitter. One online campaign is very popular among the people of the current campaign with the hashtag #2019GantiPresiden. In studies sentiment analysis required hashtag 2019GantiPresiden classifier and the selection of robust functionality that mendaptkan high accuracy values. One of the classifier and feature selection algorithms are Naive Bayes classifier (NBC) with Tri-Gram feature selection Character & Term-Frequency which previous research has resulted in a fairly high accuracy. The purpose of this study was to determine the implementation of Algorithm Naive Bayes classifier (NBC) with each selection and compare features and get accurate results from Algorithm Naive Bayes classifier (NBC) with both the selection of the feature. The author uses the method of observation to collect data and do the simulation. By using the data of 1,000 tweets originating from hashtag # 2019GantiPresiden taken on 15 September 2018, the author divides into two categories: 950 tweets as training data and 50 tweets as test data where the labeling process using methods Lexicon Based sentiment. From this study showed Naïve Bayes classifier algorithm accuracy (NBC) with feature selection Character Tri-Gram by 76% and Term-Frequency by 74%,the result show that the feature selection Character Tri-Gram better than Term-Frequency.