Sentiment analysis on public opinion of electric vehicles usage in Indonesia using support vector machine algorithms
Technological developments in the automotive industry have experienced significant progress in recent years. Currently, many electric vehicles are being produced as an environmentally friendly alternative to vehicles. The use of electric vehicles has become an intense topic of conversation in societ...
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| Format: | Article |
| Language: | English |
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Universitas Sultan Ageng Tirtayasa
2023-09-01
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| Series: | Teknika |
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| Online Access: | https://jurnal.untirta.ac.id/index.php/ju-tek/article/view/21967 |
| _version_ | 1797363256984076288 |
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| author | Naufal Avilandi Poedjimartojo Dita Pramesti Riska Yanu Fa’rifah |
| author_facet | Naufal Avilandi Poedjimartojo Dita Pramesti Riska Yanu Fa’rifah |
| author_sort | Naufal Avilandi Poedjimartojo |
| collection | DOAJ |
| description | Technological developments in the automotive industry have experienced significant progress in recent years. Currently, many electric vehicles are being produced as an environmentally friendly alternative to vehicles. The use of electric vehicles has become an intense topic of conversation in society, giving rise to various responses and opinions on Twitter. This research aims to analyze Indonesian people's sentiment regarding using electric vehicles through data collected from Twitter. Sentiment analysis is carried out using a machine-learning approach. The best method for pattern recognition problems is a Support Vector Machine (SVM) to sort each comment into positive or negative sentiments. Meanwhile, SVM classification performance was measured using the Confusion Matrix method. In this research, the Synthetic Minority Over-Sampling Technique (SMOTE) method and the Random Undersampling (RUS) method were used to overcome data imbalance. After the model creation and performance evaluation process, the best model produced was the baseline Support Vector Machine with a data sharing ratio of 70:30 without applying imbalance handling techniques. This model achieved an accuracy of 94.8%, a precision value of 95.5%, a recall value of 99.1%, and an F-1 Score value of 97.2%.<div id="urban-overlay" style="left: -10px; top: -10px; width: 0px; height: 0px;"> </div> |
| first_indexed | 2024-03-08T16:18:43Z |
| format | Article |
| id | doaj.art-bd8f86402bec47fd9ecc91fa8a4c99d8 |
| institution | Directory Open Access Journal |
| issn | 1693-024X 2654-4113 |
| language | English |
| last_indexed | 2024-03-08T16:18:43Z |
| publishDate | 2023-09-01 |
| publisher | Universitas Sultan Ageng Tirtayasa |
| record_format | Article |
| series | Teknika |
| spelling | doaj.art-bd8f86402bec47fd9ecc91fa8a4c99d82024-01-07T12:27:39ZengUniversitas Sultan Ageng TirtayasaTeknika1693-024X2654-41132023-09-0119215216010.36055/tjst.v19i2.2196710390Sentiment analysis on public opinion of electric vehicles usage in Indonesia using support vector machine algorithmsNaufal Avilandi Poedjimartojo0Dita Pramesti1Riska Yanu Fa’rifah2Telkom UniversityTelkom UniversityTelkom UniversityTechnological developments in the automotive industry have experienced significant progress in recent years. Currently, many electric vehicles are being produced as an environmentally friendly alternative to vehicles. The use of electric vehicles has become an intense topic of conversation in society, giving rise to various responses and opinions on Twitter. This research aims to analyze Indonesian people's sentiment regarding using electric vehicles through data collected from Twitter. Sentiment analysis is carried out using a machine-learning approach. The best method for pattern recognition problems is a Support Vector Machine (SVM) to sort each comment into positive or negative sentiments. Meanwhile, SVM classification performance was measured using the Confusion Matrix method. In this research, the Synthetic Minority Over-Sampling Technique (SMOTE) method and the Random Undersampling (RUS) method were used to overcome data imbalance. After the model creation and performance evaluation process, the best model produced was the baseline Support Vector Machine with a data sharing ratio of 70:30 without applying imbalance handling techniques. This model achieved an accuracy of 94.8%, a precision value of 95.5%, a recall value of 99.1%, and an F-1 Score value of 97.2%.<div id="urban-overlay" style="left: -10px; top: -10px; width: 0px; height: 0px;"> </div>https://jurnal.untirta.ac.id/index.php/ju-tek/article/view/21967electric vehicletwittersentimen analysissupport vector machineoversamplingundersamplingsmoterus |
| spellingShingle | Naufal Avilandi Poedjimartojo Dita Pramesti Riska Yanu Fa’rifah Sentiment analysis on public opinion of electric vehicles usage in Indonesia using support vector machine algorithms Teknika electric vehicle sentimen analysis support vector machine oversampling undersampling smote rus |
| title | Sentiment analysis on public opinion of electric vehicles usage in Indonesia using support vector machine algorithms |
| title_full | Sentiment analysis on public opinion of electric vehicles usage in Indonesia using support vector machine algorithms |
| title_fullStr | Sentiment analysis on public opinion of electric vehicles usage in Indonesia using support vector machine algorithms |
| title_full_unstemmed | Sentiment analysis on public opinion of electric vehicles usage in Indonesia using support vector machine algorithms |
| title_short | Sentiment analysis on public opinion of electric vehicles usage in Indonesia using support vector machine algorithms |
| title_sort | sentiment analysis on public opinion of electric vehicles usage in indonesia using support vector machine algorithms |
| topic | electric vehicle sentimen analysis support vector machine oversampling undersampling smote rus |
| url | https://jurnal.untirta.ac.id/index.php/ju-tek/article/view/21967 |
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