Comparison of SVM & Naïve Bayes Methods in Sentiment Analysis of Electric Vehicle Subsidy Policy Based on X Data

Authors

  • I Wayan Darma Wiguna Faculty Of Information Engineering, Institut Bisnis dan Teknologi Indonesia
  • Devi Valentino Waas
  • I Komang Arya Ganda Wiguna
  • Made Leo Radhitya

DOI:

https://doi.org/10.23960/jesr.v6i1.158 - Abstract View: 203

Keywords:

Electric Vehicle Subsidy Policy, Sentiment Analysis, SVM, Naïve Bayes, Social Media X

Abstract

The policy of subsidizing electric vehicles has become a widely discussed issue on social media platform X. The provision of electric vehicle subsidies by the Indonesian government aims to stimulate higher adoption of electric vehicles, with the overarching goal of mitigating air pollution. However, the presence of electric vehicle subsidies continues to elicit both support and opposition among the public. On social media platform X, there is a wealth of data suitable for text mining, particularly concerning the current hot topic of electric vehicle subsidies. This research aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes methods in conducting sentiment analysis on discussions related to the electric vehicle subsidy policy on social media platform X. The testing technique involves using 20% of the total dataset, comprising 5553 data points, and employing 10-fold cross-validation. The results from the 20% test data indicate that the Support Vector Machine (SVM) method's confusion matrix performance is superior, with the highest values achieved using the RBF kernel: accuracy 83.02%, precision 84.61%, and recall 83.02%. In the performance evaluation testing with 10-fold cross-validation, the Support Vector Machine (SVM) method outperforms, especially with the RBF kernel, yielding an average accuracy of 82.88% over 10 iterations.

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Author Biographies

I Wayan Darma Wiguna, Faculty Of Information Engineering, Institut Bisnis dan Teknologi Indonesia

The policy of subsidizing electric vehicles has become a widely discussed issue on social media platform X. The provision of electric vehicle subsidies by the Indonesian government aims to stimulate higher adoption of electric vehicles, with the overarching goal of mitigating air pollution. However, the presence of electric vehicle subsidies continues to elicit both support and opposition among the public. On social media platform X, there is a wealth of data suitable for text mining, particularly concerning the current hot topic of electric vehicle subsidies. This research aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes methods in conducting sentiment analysis on discussions related to the electric vehicle subsidy policy on social media platform X. The testing technique involves using 20% of the total dataset, comprising 5553 data points, and employing 10-fold cross-validation. The results from the 20% test data indicate that the Support Vector Machine (SVM) method's confusion matrix performance is superior, with the highest values achieved using the RBF kernel: accuracy 83.02%, precision 84.61%, and recall 83.02%. In the performance evaluation testing with 10-fold cross-validation, the Support Vector Machine (SVM) method outperforms, especially with the RBF kernel, yielding an average accuracy of 82.88% over 10 iterations.

Devi Valentino Waas

The policy of subsidizing electric vehicles has become a widely discussed issue on social media platform X. The provision of electric vehicle subsidies by the Indonesian government aims to stimulate higher adoption of electric vehicles, with the overarching goal of mitigating air pollution. However, the presence of electric vehicle subsidies continues to elicit both support and opposition among the public. On social media platform X, there is a wealth of data suitable for text mining, particularly concerning the current hot topic of electric vehicle subsidies. This research aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes methods in conducting sentiment analysis on discussions related to the electric vehicle subsidy policy on social media platform X. The testing technique involves using 20% of the total dataset, comprising 5553 data points, and employing 10-fold cross-validation. The results from the 20% test data indicate that the Support Vector Machine (SVM) method's confusion matrix performance is superior, with the highest values achieved using the RBF kernel: accuracy 83.02%, precision 84.61%, and recall 83.02%. In the performance evaluation testing with 10-fold cross-validation, the Support Vector Machine (SVM) method outperforms, especially with the RBF kernel, yielding an average accuracy of 82.88% over 10 iterations.

I Komang Arya Ganda Wiguna

The policy of subsidizing electric vehicles has become a widely discussed issue on social media platform X. The provision of electric vehicle subsidies by the Indonesian government aims to stimulate higher adoption of electric vehicles, with the overarching goal of mitigating air pollution. However, the presence of electric vehicle subsidies continues to elicit both support and opposition among the public. On social media platform X, there is a wealth of data suitable for text mining, particularly concerning the current hot topic of electric vehicle subsidies. This research aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes methods in conducting sentiment analysis on discussions related to the electric vehicle subsidy policy on social media platform X. The testing technique involves using 20% of the total dataset, comprising 5553 data points, and employing 10-fold cross-validation. The results from the 20% test data indicate that the Support Vector Machine (SVM) method's confusion matrix performance is superior, with the highest values achieved using the RBF kernel: accuracy 83.02%, precision 84.61%, and recall 83.02%. In the performance evaluation testing with 10-fold cross-validation, the Support Vector Machine (SVM) method outperforms, especially with the RBF kernel, yielding an average accuracy of 82.88% over 10 iterations.

Made Leo Radhitya

The policy of subsidizing electric vehicles has become a widely discussed issue on social media platform X. The provision of electric vehicle subsidies by the Indonesian government aims to stimulate higher adoption of electric vehicles, with the overarching goal of mitigating air pollution. However, the presence of electric vehicle subsidies continues to elicit both support and opposition among the public. On social media platform X, there is a wealth of data suitable for text mining, particularly concerning the current hot topic of electric vehicle subsidies. This research aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes methods in conducting sentiment analysis on discussions related to the electric vehicle subsidy policy on social media platform X. The testing technique involves using 20% of the total dataset, comprising 5553 data points, and employing 10-fold cross-validation. The results from the 20% test data indicate that the Support Vector Machine (SVM) method's confusion matrix performance is superior, with the highest values achieved using the RBF kernel: accuracy 83.02%, precision 84.61%, and recall 83.02%. In the performance evaluation testing with 10-fold cross-validation, the Support Vector Machine (SVM) method outperforms, especially with the RBF kernel, yielding an average accuracy of 82.88% over 10 iterations.

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Published

2024-06-28

How to Cite

[1]
I. W. D. . Wiguna, D. V. . Waas, I. K. A. G. . Wiguna, and M. L. . Radhitya, “Comparison of SVM & Naïve Bayes Methods in Sentiment Analysis of Electric Vehicle Subsidy Policy Based on X Data”, JESR, vol. 6, no. 1, pp. 23 –, Jun. 2024.

Issue

Vol. 6 No. 1 (2024)

Section

Articles

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Copyright © JESR (Journal of Engineering and Scientific Research). This article is an open access article distributed under a CC-BY Creative Commons Attribution 4.0 International License, as presented on the link https://creativecommons.org/licenses/by/4.0/deed.