TELKOMNIKA Telecommunication, Computing, Electronics and Control
Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization
Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization
Subject
Cosine similarity, Hoax news detection, Naïve Bayes, Particle swarm optimization, sentiment analysis
Description
Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke
readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.
readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.
Creator
Heru Agus Santoso, Eko Hari Rachmawanto, Adhitya Nugraha, Akbar Aji Nugroho, De Rosal Ignatius Moses Setiadi, Ruri Suko Basuki
Source
DOI: 10.12928/TELKOMNIKA.v18i2.14744
Publisher
Universitas Ahmad Dahlan
Date
April 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Heru Agus Santoso, Eko Hari Rachmawanto, Adhitya Nugraha, Akbar Aji Nugroho, De Rosal Ignatius Moses Setiadi, Ruri Suko Basuki , “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3679.
Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3679.