SVM and Naïve Bayes Algorithm Comparison for User Sentiment Analysis on Twitter


  • Rahmat Syahputra Universitas Labuhanbatu, Indonesia
  • Gomal Juni Yanris Universitas Labuhanbatu, Indonesia
  • Deci Irmayani Universitas Labuhanbatu, Indonesia




With the emergence of the Peduli Protect application, which is used by the government to monitor the spread of Covid-19 in Indonesia, it turns out to be reaping the pros and cons of public opinion on Twitter. From this phenomenon, a research was conducted by mapping the sentiment analysis of twitter users towards the Peduli Protect application. This study aims to compare two classification algorithms that are included in the supervised learning category. The two algorithms are Support Vector Machine (SVM) and Naïve Bayes. The two algorithms are implemented in analyzing the sentiment analysis of twitter user reviews on the Peduli Protect application. The dataset used in this research is tweets of twitter users with a total of 4,782 tweets. Then, compared to how much accuracy and processing time required of the two algorithms. The stages of the method in this research are: collecting data from user tweets with a crawling technique, preprocessing text, weighting words using the TF-IDF method, classification using the SVM and Naïve Bayes algorithm, k-fols cross validation test, and drawing conclusions. The results showed that the accuracy of the SMV algorithm with the k-fold test method was 86% and the split 8020 technique resulted in an accuracy of 79%. Meanwhile, the Naïve Bayes algorithm produces an accuracy of 85% with k-fold, and an accuracy of 80% with a split 8020. From these results it can be concluded that both algorithms have the same level of accuracy, only different in processing time, where Naïve Bayes algorithm is faster with time required 0.0094 seconds.

GS Cited Analysis


Download data is not yet available.


Fitriana, F., Utami, E., & Al Fatta, H. (2021). Analisis Sentimen Opini Terhadap Vaksin Covid - 19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes. Jurnal Komtika (Komputasi Dan Informatika), 5(1), 19–25.

Goyal, G. (2021). Twitter Sentiment Analysis- A NLP Use-Case for Beginners. Retrieved April 25, 2022, from Analytics Vidhya website: analysis refers to identifying,about a variety of topics.

Hadna, N. M., Santosa, P., & Winarno, W. (2016). Studi Literatur Tentang Perbandingan Metode Untuk Proses Analisis Sentimen di Twitter.

Ibrahim, M., Abdillah, O., Wicaksono, A. F., & Adriani, M. (2015). Buzzer Detection and Sentiment Analysis for Predicting Presidential Election Results in a Twitter Nation. 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 1348–1353.

Illia, F., Eugenia, M. P., & Rutba, S. A. (2021). Sentiment Analysis on PeduliLindungi Application Using TextBlob and VADER Library. Proceedings of The International Conference on Data Science and Official Statistics, 1(1), 278–288.

Iskandar, J. W., & Nataliani, Y. (2021). Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(6), 1120–1126.

Kristiyanti, D. A., Umam, A. H., Wahyudi, M., Amin, R., & Marlinda, L. (2018). Comparison of SVM & Naive Bayes Algorithm for Sentiment Analysis Toward West Java Governor Candidate Period 2018-2023 Bases on Public Opinion on Twitter. 2018 6th International Conference on Cyber and IT Service Management (CITSM), 1–6.

Kurniawan, S., Gata, W., Puspitawati, D. A., Nurmalasari, Tabrani, M., & Novel, K. (2019). Perbandingan Metode Klasifikasi Analisis Sentimen Tokoh Politik Pada Komentar Media Berita Online. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(2), 176–183.

Mustopa, A., Hermanto, Anna, Pratama, E. B., Hendini, A., & Risdiansyah, D. (2020). Analysis of User Reviews for the PeduliLindungi Application on Google Play Using the Support Vector Machine and Naive Bayes Algorithm Based on Particle Swarm Optimization. 2020 Fifth International Conference on Informatics and Computing (ICIC), 1–7.

Negara, A. B. P., Muhardi, H., & Putri, I. M. (2020). Analisis Sentimen Maskapai Penerbangan Menggunakan Metode Naive Bayes dan Seleksi Fitur Information Gain. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(3), 599.

Rahat, A. M., Kahir, A., & Masum, A. K. M. (2019). Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset. 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), 266–270.

Rolliawati, D., Khalid, K., & Rozas, I. S. (2020). Teknologi Opinion Mining untuk Mendukung Strategic Planning. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(2), 293.

Siswanto, Wibawa, Y. P., Gata, W., Gata, G., & Kusumawardhani, N. (2018). Classification Analysis of MotoGP Comments on Media Social Twitter Using Algorithm Support Vector Machine and Naive Bayes. 2018 International Conference on Applied Information Technology and Innovation (ICAITI), 96–101.

Tuhuteru, H., & Iriani, A. (2018). Analisis Sentimen Perusahaan Listrik Negara Cabang Ambon Menggunakan Metode Support Vector Machine dan Naive Bayes Classifier. Jurnal Informatika: Jurnal Pengembangan IT, 3, 394–401.

Yaakub, M. R., Latiffi, M. I. A., & Safra, L. (2019). A Review on Sentiment Analysis Techniques and Applications. IOP Conference Series: Materials Science and Engineering, 551, 12070.


Crossmark Updates

How to Cite

Syahputra, R., Yanris, G. J. ., & Irmayani, D. . (2022). SVM and Naïve Bayes Algorithm Comparison for User Sentiment Analysis on Twitter. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(2), 671-678.

Most read articles by the same author(s)

1 2 3 > >>