Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IJoICT (International Journal on Information and Communication Technology)
Herman, Fizio Ramadhan
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Buzzer Account Detection in Political Hate Tweets: Case Study of the Indonesian Presidential Election 2024 Herman, Fizio Ramadhan; Ade Romadhony
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1012

Abstract

The Indonesian Presidential Election of 2024 has seen a widespread use of social media such as Twitter for political campaigning and discussion. However, this has also enabled the spread of hate speech from buzzer accounts that are created to influence public opinions. This study implements a machine learning approach to classify buzzer accounts that are spreading hate speeches during the presidential election period. By utilizing IndoBERT for hate speech classification and a traditional machine learning model to classify buzzer accounts. This study analyzes 62,341 tweets for hate speech classification and 961 accounts for buzzer account classification. Our implementation of IndoBERT achieved a strong performance with 91.12% of precision and recall, and 91.19\% accuracy and F1-score in hate speech classification. While for buzzer account classification, we compared Decision Tree, Random Forest, and XGBoost, with Decision Tree achieving the highest performance of 64% precision, recall, accuracy, and F1-Score. Our results demonstrate the effectiveness of combining deep learning for hate speech classification with traditional machine learning for buzzer account classification, contributing to the development of more effective content filtering for election discourse on social media.
Co-Authors Ade Romadhony