Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes

  • Merinda Lestandy Universitas Muhammadiyah Malang
  • Abdurrahim Abdurrahim Universitas Muhammadiyah Malang
  • Lailis Syafa’ah Universitas Muhammadiyah Malang
Keywords: Analisis Sentimen, Vaksin COVID-19, TF-IDF, RNN, Naïve Bayes


COVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtained from 5000 COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of pre-processing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of 97.77% compared to Naïve Bayes (TF-IDF) of 80%.



Download data is not yet available.


G. Peretto, S. Sala, and A. L. P. Caforio, “The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak – an update on the status,” Eur. Heart J., vol. 41, no. 22, pp. 2124–2125, 2020, doi: 10.1093/eurheartj/ehaa396.

L. Y. C. Wong and J. Burkell, “Motivations for sharing news on social media,” ACM Int. Conf. Proceeding Ser., vol. Part F1296, 2017, doi: 10.1145/3097286.3097343.

Y. Shi et al., “Knowledge and attitudes of medical staff in Chinese psychiatric hospitals regarding COVID-19,” Brain, Behav. Immun. - Heal., vol. 4, p. 100064, 2020, doi: 10.1016/j.bbih.2020.100064.

M. A. Fauzi, “Random forest approach fo sentiment analysis in Indonesian language,” Indones. J. Electr. Eng. Comput. Sci., vol. 12, no. 1, pp. 46–50, 2018, doi: 10.11591/ijeecs.v12.i1.pp46-50.

W. K. Sari, D. P. Rini, and R. F. Malik, “Text Classification Using Long Short-Term Memory With GloVe Features,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 5, no. 2, p. 85, 2020, doi: 10.26555/jiteki.v5i2.15021.

W. K. Sari, D. P. Rini, R. F. Malik, and I. S. B. Azhar, “Klasifikasi Teks Multilabel pada Artikel Berita Menggunakan Long Short- Term Memory dengan Word2Vec,” Resti, vol. 1, no. 10, pp. 276–285, 2017.

M. Hung et al., “Social network analysis of COVID-19 sentiments: Application of artificial intelligence,” J. Med. Internet Res., vol. 22, no. 8, 2020, doi: 10.2196/22590.

T. Hendrawati and C. P. Yanti, “Analysis of Twitter Users Sentiment against the Covid-19 Outbreak Using the Backpropagation Method with Adam Optimization,” J. Electr. Electron. Informatics, vol. 5, no. 1, p. 1, 2021, doi: 10.24843/jeei.2021.v05.i01.p01.

A. K. Fauziyyah, “Analisis Sentimen Pandemi Covid19 Pada Streaming Twitter Dengan Text Mining Python,” J. Ilm. SINUS, vol. 18, no. 2, p. 31, 2020, doi: 10.30646/sinus.v18i2.491.

N. Chintalapudi, G. Battineni, and F. Amenta, “Sentimental analysis of COVID-19 tweets using deep learning models,” Infect. Dis. Rep., vol. 13, no. 2, pp. 329–339, 2021, doi: 10.3390/IDR13020032.

T. Vijay, A. Chawla, B. Dhanka, and P. Karmakar, “Sentiment Analysis on COVID-19 Twitter Data,” 2020 5th IEEE Int. Conf. Recent Adv. Innov. Eng. ICRAIE 2020 - Proceeding, vol. 2020, no. November 2019, 2020, doi: 10.1109/ICRAIE51050.2020.9358301.

J. V. Lazarus et al., “A global survey of potential acceptance of a COVID-19 vaccine,” Nat. Med., vol. 27, no. 2, pp. 225–228, 2021, doi: 10.1038/s41591-020-1124-9.

K. M. Bubar et al., “Model-informed COVID-19 vaccine prioritization strategies by age and serostatus,” Science (80-. )., vol. 371, no. 6532, pp. 916–921, 2021, doi: 10.1126/science.abe6959.

F. F. Rachman and S. Pramana, “Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter,” Heal. Inf. Manag. J., vol. 8, no. 2, pp. 100–109, 2020, [Online]. Available:

C.- Pandemic, B. Laurensz, and E. Sediyono, “Analisis Sentimen Masyarakat terhadap Tindakan Vaksinasi dalam Upaya Mengatasi Pandemi Covid-19 ( Analysis of Public Sentiment on Vaccination in Efforts to Overcome the,” vol. 10, no. 2, pp. 118–123, 2021.

D. A. Nurdeni, I. Budi, and A. B. Santoso, “Sentiment Analysis on Covid19 Vaccines in Indonesia: From the Perspective of Sinovac and Pfizer,” 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, no. April, pp. 122–127, 2021, doi: 10.1109/EIConCIT50028.2021.9431852.

F. W. Ramadhan, H. T. Sukmana, L. K. Oh, and ..., “Analysis Of Warganet Comments On It Services In Mandiri Bank Using K-Nearest Neighbor (K-Nn) Algorithm Based On Itsm Criteria,” ADI Journal on Recent …., 2019, [Online]. Available:

K. Setiawan, B. Rahmatullah, and ..., “Komparasi Metode Naive Bayes Dan Support Vector Machine Menggunakan Particle Swarm Optimization Untuk Analisis Sentimen …,” J. …, 2020, [Online]. Available:

M. Cindo, D. P. Rini, and E. Ermatita, “Literatur Review: Metode Klasifikasi Pada Sentimen Analisis,” Semin. Nas. Teknol. …, 2019, [Online]. Available:

A. F. Anees, A. Shaikh, A. Shaikh, and S. Shaikh, “Survey Paper on Sentiment Analysis : Techniques and Challenges,” EasyChair, pp. 2516–2314, 2020.

N. Yadav, O. Kudale, A. Rao, S. Gupta, and A. Shitole, “Twitter Sentiment Analysis Using Supervised Machine Learning,” Lect. Notes Data Eng. Commun. Technol., vol. 57, no. April 2020, pp. 631–642, 2021, doi: 10.1007/978-981-15-9509-7_51.

Imamah and F. H. Rachman, “Twitter sentiment analysis of Covid-19 using term weighting TF-IDF and logistic regresion,” Proceeding - 6th Inf. Technol. Int. Semin. ITIS 2020, pp. 238–242, 2020, doi: 10.1109/ITIS50118.2020.9320958.

K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag, and A. E. Hassanien, “Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media,” Appl. Soft Comput. J., vol. 97, p. 106754, 2020, doi: 10.1016/j.asoc.2020.106754.

A. M. Rahat, A. Kahir, and A. K. M. Masum, “Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset,” Proc. 2019 8th Int. Conf. Syst. Model. Adv. Res. Trends, SMART 2019, pp. 266–270, 2020, doi: 10.1109/SMART46866.2019.9117512.

M. Wongkar and A. Angdresey, “Sentiment analysis using Naive Bayes Algorithm of the data crawler: Twitter,” 2019 Fourth Int. …, 2019, [Online]. Available:

G. A. Ruz, P. A. Henríquez, and A. Mascareño, “Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers,” Futur. Gener. Comput. Syst., vol. 106, pp. 92–104, 2020, doi: 10.1016/j.future.2020.01.005.

L. Kurniasari and A. Setyanto, “Sentiment Analysis using Recurrent Neural Network,” J. Phys. Conf. Ser., vol. 1471, no. 1, 2020, doi: 10.1088/1742-6596/1471/1/012018.

B. N. Saha, A. Senapati, and A. Mahajan, “LSTM based Deep RNN Architecture for Election Sentiment Analysis from Bengali Newspaper,” 2020 Int. Conf. Comput. Perform. Eval. ComPE 2020, pp. 564–569, 2020, doi: 10.1109/ComPE49325.2020.9200062.

M. F. Wahid, M. J. Hasan, and M. S. Alom, “Cricket Sentiment Analysis from Bangla Text Using Recurrent Neural Network with Long Short Term Memory Model,” 2019 Int. Conf. Bangla Speech Lang. Process. ICBSLP 2019, no. September, pp. 27–28, 2019, doi: 10.1109/ICBSLP47725.2019.201500.

M. Atikur Rahman and E. K. Dey, “Datasets for aspect-based sentiment analysis in bangla and its Baseline evaluation,” Data, vol. 3, no. 2, 2018, doi: 10.3390/data3020015.

M. Savargiv, B. Masoumi, and M. R. Keyvanpour, “A new random forest algorithm based on learning automata,” Comput. Intell. Neurosci., vol. 2021, 2021, doi: 10.1155/2021/5572781.

A. M. Pravina, I. Cholissodin, and P. P. Adikara, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM),” … Teknol. Inf. dan Ilmu …, 2019, [Online]. Available:

M. Allahyari et al., “A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques,” 2017, [Online]. Available:

S. Ghosh and M. S. Desarkar, “Class Specific TF-IDF Boosting for Short-text Classification,” pp. 1629–1637, 2018, doi: 10.1145/3184558.3191621.

A. Z. Amrullah, A. S. Anas, and ..., “Analisis Sentimen Movie Review Menggunakan Naive Bayes Classifier Dengan Seleksi Fitur Chi Square,” J. …, 2020, [Online]. Available:

M. Wongkar and A. Angdresey, “Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter,” Proc. 2019 4th Int. Conf. Informatics Comput. ICIC 2019, pp. 1–5, 2019, doi: 10.1109/ICIC47613.2019.8985884.

How to Cite
Merinda Lestandy, Abdurrahim Abdurrahim, & Lailis Syafa’ah. (2021). Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 802 - 808.
Artikel Rekayasa Sistem Informasi