Analisis Sentimen Masyarakat Indonesia terhadap Pemindahan Ibu Kota Negara Indonesia pada Twitter
The relocation state capital of Indonesia raises various responses, especially from the Indonesian people. The discussion related to these issues is very interesting to study, how are the positive and negative sentiments of the Indonesian towards the government's decision. This study aims to a...
Saved in:
Main Authors: | , , |
---|---|
Format: | Article |
Language: | Indonesian |
Published: |
Program Studi Sistem Informasi, Universitas Islam Negeri Raden Fatah Palembang
2022-06-01
|
Series: | Jurnal Sistem Informasi |
Subjects: | |
Online Access: | https://jurnal.radenfatah.ac.id/index.php/jusifo/article/view/12116 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | The relocation state capital of Indonesia raises various responses, especially from the Indonesian people. The discussion related to these issues is very interesting to study, how are the positive and negative sentiments of the Indonesian towards the government's decision. This study aims to analyze the sentiments of the Indonesian people regarding the relocation state capital of Indonesia, including the chosen name of Nusantara on Twitter. In this study, a comparison of 3 algorithms is used, namely the Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbor (KNN) algorithms. From this study, the results obtained are 1,141 positive comments, while negative sentiments are 591 comments. This shows that the Indonesian people have a positive opinion towards the new capital city of Indonesia. In the classification and model testing phase, 10-fold cross validation is used. From these tests, the SVM algorithm obtained an accuracy value of 85.71%, the Naïve Bayes algorithm obtained an accuracy value of 76.70%, the KNN algorithm obtained an accuracy value of 52.74%. This study shows that the SVM algorithm can work better than the Naïve Bayes algorithm and KNN. The accuracy value for the KNN algorithm obtains a low value, this is because the KNN algorithm is sensitive to features that are less relevant. |
---|---|
ISSN: | 2460-092X 2623-1662 |