Article Per Year (5 Year)
p-Index From 2020 - 2025
0.702
P-Index
This Author published in this journals
All Journal Jurnal Optimasi Sistem Industri Journal of Computer Engineering, Electronics and Information Technology Indonesian Journal of Innovation Multidisipliner Research Indonesian Journal of Innovation Multidisipliner Research
Mantau, Aprinaldi Jasa
Unknown Affiliation
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Optimasi Sistem Industri

 1 
Feature Selection and Performance Evaluation of Buzzer Classification Model Afra, Dian Isnaeni Nurul; Fajri, Radhiyatul; Prafitia, Harnum Annisa; Arief, Ikhwan; Mantau, Aprinaldi Jasa
Jurnal Optimasi Sistem Industri Vol. 23 No. 1 (2024): Published in July 2024
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.919 KB) | DOI: 10.25077/josi.v23.n1.p1-14.2024

Abstract

In the rapidly evolving digital age, social media platforms have transformed into battleground for shaping public opinion. Among these platforms, X has been particularly susceptible to the phenomenon of 'buzzers', paid or coordinated actors who manipulate online discussions and influence public sentiment. This manipulation poses significant challenges for users, researchers, and policymakers alike, necessitating robust detection measures and strategic feature selection for accurate classification models. This research explores the utilization of various feature selection techniques to identify the most influential features among the 24 features employed in the classification modeling using Support Vector Machine. This study found that selecting 11 key features yields a remarkably effective classification model, achieving an impressive F1-score of 87.54 in distinguishing between buzzer and non-buzzer accounts. These results suggest that focusing on the relevant features can improve the accuracy and efficiency of buzzer detection models. By providing a more robust and adaptable solution to buzzer detection, our research has the potential to advance social media research and policy. This enabling researchers and policymakers to devise strategies aimed at mitigating misinformation dissemination and cultivating an environment of trust and integrity within social media platforms, thus fostering healthier online interactions and discourse.
Co-Authors Afra, Dian Isnaeni Nurul Ahmad, Zindhu Maulana Akahoshia, Tomohiko Ari Wibawa Budi Santosa C. I. Sutrisno Fajri, Radhiyatul Harlinanda, Daffa Alifian Ikhwan Arief Jiea, Guo Muhammad Khalil, Muhammad Murata, Masaharu Narahara, Sayoko Pradana, Ayu Nabila Kusuma Prafitia, Harnum Annisa Projek Priyonggo Sumangun Lukitadi Umasangadji, Fahmi ZHANG, Shuo