Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK)
Kinanti, Virli Galuh
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

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

Found 1 Documents
Search

 1 
Pengaruh Penggunaan Emoji Pada Tingkat Akurasi Sentimen Di Twitter Menggunakan Metode Support Vector Machine Dharmawan, Tio; Kinanti, Virli Galuh; Maududie, Achmad
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7046

Abstract

Opinions and preferences expressed on social media and microblogging services are very important for sentiment analysis. A Support Vector Machine (SVM) is a learning system that uses a hypothetical space in the form of a linear function in a high dimensional feature space and applies a learning bias derived from statistical learning theory. The accuracy results obtained by the Support Vector Machine method from the first topic, namely booster vaccines as a homecoming requirement, were 65% for text only and 69% for text containing emoji. The accuracy results for the second discussion topic, namely demonstrations against Jokowi for 3 periods, were 79% for text only and 82% for text containing emoji. As for the third topic regarding the scarcity of cooking oil and rising fuel prices, the accuracy obtained is 74% for text only and 76% for text containing emojis.
Co-Authors Achmad Maududie Dharmawan, Tio