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All Journal Building of Informatics, Technology and Science
Sinta Wahyuni Ritonga
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT

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Klasifikasi Sentimen Masyarakat di Twitter terhadap Ganjar Pranowo dengan Metode Naïve Bayes Classifier Sinta Wahyuni Ritonga; Yusra .; Muhammad Fikry; Eka Pandu Cynthia
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3535

Abstract

Indonesia is a country with a Democratic political system. The public is given freedom of speech, collaboration and public criticism. In the modern era, the use of social media is growing rapidly at the community level. One of the social media trends in Indonesia is Twitter which is used to convey aspirations to the government and as a means to convey daily activities, opinions, culture and get the latest information or news from Indonesia and abroad. Public opinion taken from Twitter can be positive, negative and neutral. The number of tweets on Twitter one of the trend topics in Indonesia is Ganjar Pranowo, can be used as a source of data in the assessment of sentiment classification which is processed to produce accuracy values. This study aims to classify public opinion on social media Twitter about Ganjar Pranowo using Naïve Bayes Classifier method. In the classification processing using a dataset of 4000 tweet data with two labeling classes, positive and negative to determine the efficiency of NBC performance combined with TF-IDF weighting, feature selection using supervised learning approach techniques. The results of the test on the classification of public sentiment research on Twitter about Ganjar Pranowo using NBC method using 10% of the test data from the dataset used to produce an accuracy value of 83.0%.
Co-Authors Eka Pandu Cynthia Muhammad Fikry Yusra .