Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science
Rizky, Fariz Muhammad
Unknown Affiliation
Computer Science & IT

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

Found 1 Documents
Search

 1 
Twitter Sentiment Analysis of Kanjuruhan Disaster using Word2Vec and Support Vector Machine Rizky, Fariz Muhammad; Jondri, Jondri; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3612

Abstract

The Kanjuruhan disaster on 1 October 2022, gained the peoples attention. People share their thoughts on social media. Their posts contain a variety of perspectives. Sentiment analysis is possible to use on a dataset of people's posts. This final project applies the supervised learning Support Vector Machine (SVM) method with feature expansion using Word2Vec and TF-IDF as weighting. Three SVM kernels—rbf, linear, and polynomial—are applied. Three split data techniques and two different types of training data are used to train each kernel. Training data with oversampling and training data without oversampling are the two types of training data. The best result gained from using rbf kernel, split ratio 70:30, and oversampling. From it, oversampling trained model have relatively stable in every split rasio and kernel without having significant difference.
Co-Authors Jondri Jondri Kemas Muslim Lhaksmana