Analisis Sentimen Masyarakat Di Media Sosial X Terhadap Kemenkes Dengan Naive Bayes dan SVM

Authors

  • Freddy Andrew Ryandi Universitas Trisakti
  • Dian Pratiwi Universitas Trisakti
  • Syandra Sari Universitas Trisakti

Keywords:

SVM, Naïve Bayes, Sentiment Analysis, Social Media X, COVID-19

Abstract

 This study examines public sentiment on social media platform X regarding Indonesia's Ministry of Health during the COVID-19 pandemic, using Naïve Bayes and Support Vector Machine (SVM) algorithms. Posts mentioning the Ministry’s official account (@KemenkesRI) were preprocessed and labeled using the VADER tool. Sentiment classification was performed with TF-IDF word weighting, and both algorithms were evaluated. Results show SVM achieved slightly higher accuracy (79%) than Naïve Bayes (77%), indicating its effectiveness in handling complex language structures, though it requires more computational resources. This research underscores the utility of SVM for analyzing public sentiment on health policies..



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Author Biography

Syandra Sari, Universitas Trisakti

Sistem Informasi

References

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Published

2025-01-21

How to Cite

Andrew Ryandi, F. ., Pratiwi, D., & Sari, S. (2025). Analisis Sentimen Masyarakat Di Media Sosial X Terhadap Kemenkes Dengan Naive Bayes dan SVM . Jurnal Sains Dan Teknologi, 7(1), 1-6. Retrieved from https://ejournal.sisfokomtek.org/index.php/saintek/article/view/4615