Sentiment Analysis of Public on The COVID-19 (Corona Virus Disease 2019) Vaccination Moderna and Sinovac Vaccine Using Naïve Bayes

Salsyabila Vidia Nur Afni, Esi Putri Silmina

Abstract


One of the government's attempts to break the COVID-19 (Corona Virus Disease 2019) chain was vaccination. Achieving the herd imunity is the main goal of vaccinating to control the COVID-19 pandemic. The Sinovac Vaccine and the Moderna Vaccine are examples of a type of inactivation vaccine. Indonesia's role in expressing and responding to the controversy often leads to public services used as materials to analyze who produced the data in support of the decision to compare the Sinovac type vaccine with the Moderna type vaccine. The purpose of the study is to see how people respond to vaccination with the various kinds of vaccines such as those taken in the study, the Moderna Vaccine type and the Sinovac Vaccine and the perceived value of sentiment analysis on the two vaccines. The method used in the study using the Naive Bayes Classification Method. The results from the study indicate that the Twitter users’sThe results from the study indicate that the Twitter users' twitter Sentiments Analysis with a positive testing of the Sinovac is larger than the Moderna Vaccine, which is 367 for the Sinovac Vaccine and 144 for the Moderna Vaccine.


Keywords


Moderna, Naïve Bayes, Sentiment Analysis, Sinovac

References


R. Yasmin, “Covid-19 Menggunakan Metode Naive Bayes Classifier Pada Media Sosial Twitter Covid-19 Menggunakan Metode Naive Bayes,” 2021.

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.

J. Jtik, J. Teknologi, R. T. Aldisa, and M. A. Abdullah, “Analisis Sentimen Mengenai Vaksin Sinovac di Media Sosial Twitter Menggunakan Metode Naïve bayes Classification,” vol. 6, no. 3, pp. 1–5, 2022.

D. Hernikawati, “Kecenderungan Tanggapan Masyarakat Terhadap Vaksin Sinovac Berdasarkan Lexicon Based Sentiment Analysis The Trend of Public Response to Sinovac Vaccine Based on Lexicon Based Sentiment Analysis,” vol. 23, no. 1, pp. 21–31, 2021.

R. D. Septiana and A. B. Susanto, “Analisis Sentimen Vaksinasi Covid-19 Pada Twitter Menggunakan Naive Bayes Classifier Dengan Feature Selection Chi-Squared Statistic Dan Particle Swarm Optimization,” vol. V, no. September, pp. 49–56, 2021.

N. M. A. J. Astari, Dewa Gede Hendra Divayana, and Gede Indrawan, “Analisis Sentimen Dokumen Twitter Mengenai Dampak Virus Corona Menggunakan Metode Naive Bayes Classifier,” J. Sist. dan Inform., vol. 15, no. 1, pp. 27–29, 2020, doi: 10.30864/jsi.v15i1.332.

E. Nufa, “Analisis Klasifikasi Sentimen Tentang Pro Dan Kontra Masyarakat Indonesia Terhadap Vaksin Covid-19 Pada Media,” no. May, 2021.

F. Fitriana, E. Utami, and H. Al Fatta, “Analisis Sentimen Opini Terhadap Vaksin Covid-19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes,” vol. 5, no. 1, pp. 19–25, 2021.

A. Nurdiana, R. Marlina, and W. Adityasning, “Berantas Hoax Seputar Vaksin Covid-19 Melalui Kegiatan Edukasi dan Sosialisasi Vaksin Covid-19.”

L. Penelitian and H. Internal, “Hibah Internal Sistem Pendeteksi Berita Palsu ( Fake News ) Di Media Sosial,” 2019.

R. Aplikasi et al., “Jurnal Informatika Dan Teknologi Informasi P Rogram S Tudi I Nformatika – F Akultas T Eknik - U Niversitas J Anabadra,” vol. 6, no. 3, 2021.

A. Rahman et al., “Implementasi Probabilistic Neural Network Dan Word Embedding Untuk Analisis,” vol. 3, no. 2, pp. 233–242, 2021.

K. M. V. Covid-, L. P. Widayanti, F. Psikologi, U. Islam, and N. Sunan, “Hubungan Persepsi Tentang Efektifitas Vaksin Dengan Sikap Kesediaan Mengikuti Vaksinasi Covid-19 Linda Prasetyaning Widayanti 1 , Estri Kusumawati 2,” vol. 9, no. 2, pp. 78–84, 2021.

S. Lestari and S. Saepudin, “Analisis Sentimen Vaksin Sinovac Pada Twitter Menggunakan,” 2021.

H. A. K. Atau and K. Warga, “Pelaksanaan Vaksinasi Covid-19 Di Indonesia :,” vol. 10, no. April, pp. 23–41, 2021.

R. Khairani, “Strategi mix-and-match vaksin COVID-19, seberapa efektifkah?,” vol. 4, no. 3, pp. 87–89, 2021.

T. S. Pratiwi et al., “Pengaruh Media Terhadap Opini Milenial Tentang Vaksinasi,” vol. 1, no. 1, pp. 60–64, 2021.

A. K. Arianto, “Dalam Kerangka Linguistik Forensik,” pp. 115–129.

V. K. S. Que, A. Iriani, and H. D. Purnomo, “Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 9, no. 2, pp. 162–170, 2020, doi: 10.22146/jnteti.v9i2.102.

Zalyhaty, Layla, “Analisis Sentimen Tanggapan Masyarakat Terhadap Vaksin COVID-19Menggunakan Algoritma Support Vector Machine (SVM)”, 2021




DOI: http://dx.doi.org/10.28989/senatik.v7i0.451

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