Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal IJIES (International Journal of Innovation in Enterprise System)
Ramli, Muhammad Ayyub
Unknown Affiliation
Computer Science & IT Control & Systems Engineering

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

Found 1 Documents
Search

 1 
Analyzing Twitter Sentiments on Booster Vaccination with Support Vector Machine (SVM) Method Fauzi, Rahmat; Hamami, Faqih; Maulana, Fakhri Hassan; Kuswandi, Brillian Adhiyaksa; Ramli, Muhammad Ayyub
International Journal of Innovation in Enterprise System Vol. 8 No. 2 (2024): International Journal of Innovation in Enterprise System
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijies.v8i02.648

Abstract

The Indonesian government has implemented various measures to prevent the spread of the COVID-19 virus, one of which is through a vaccination program with two doses (the first dose and the second dose). However, new variants of the virus have emerged, reducing the effectiveness of the initial vaccinations. To address this, the government introduced a booster vaccination program aimed at enhancing immunity by up to 80%. The government's plan for booster vaccination has received both positive and negative opinions from the public through various media platforms, including Twitter. This study analyzes public opinions on the booster vaccination plan into three classes: positive, negative, and neutral. SVM is a classification method in machine learning categorized as supervised learning, which involves finding an optimal line (hyperplane) as a separator for two different data classes. The stages of this research include data collection, data cleaning, data transformation, and data classification using the Support Vector Machine (SVM) method. The results of this study indicate that the accuracy of the SVM model reaches 80.42%.
Co-Authors Faqih Hamami Kuswandi, Brillian Adhiyaksa Maulana, Fakhri Hassan Rahmat Fauzi