Klasifikasi Sentimen Publik Terhadap Jenis Vaksin Covid-19 yang Tersertifikasi WHO Berbasis NLP dan KNN
DOI:
https://doi.org/10.30865/mib.v7i1.5418Keywords:
Pfizer, Moderna, AstraZenecaAbstract
The corona virus epidemic became an epidemic at the end of 2019 in the world. Some people are going through a pandemic with fear or even just being normal. The expression of fear, they discussed on social media. Now, social media is a means for some people to express their emotions namelly twitter. In order to end this pandemic the company is trying to develop a covid-19 vaccine, such as Pfizer, AstraZeneca, and Moderna which have obtained licenses from the World Health Organization (WHO). However, the discovery of the vaccine was not welcomed by some people. This is because of the post-vaccine impact and the vaccine development period which is considered too short. In this study, sentiment analysis was carried out based on public sentiment on Twitter social media about the covid-19 vaccine that has obtained a license from WHO uses NLP (Natural Language Processing) and machine learning algorithms. The purpose of this research is to find out the sentiment circulating on Twitter towards WHO-certified vaccines such as Pfizer, Moderna and AstraZeneca based on NLP as decision makers and sources of reference for the general public. Based on the research results, the highest positive sentiment was the Pfizer vaccine then Moderna, namely 47.30% and 46.20%. Meanwhile, the AstraZeneca vaccine received the lowest sentiment rating of the three, namely 40.09%.
References
N. R. Yunus and A. Rezki, “Kebijakan Pemberlakuan Lockdown Sebagai Antisipasi Penyebaran Corona Virus Covid-19,†J. Sos. Budaya Syar-i, vol. 7, no. April, 2020.
Y. Dong et al., “Epidemiological Characteristics of 2143 Pediatric Patients With 2019 Coronavirus Disease in China Epidemiology of COVID-19,†Pediatrics, 2020.
I. M. Agung, “Memahami Pandemi COVID-19 dalam Perspektif Psikologi Sosial,†Psikobuletin Bul. Ilm. Psikol., vol. 1, no. 2, pp. 68–84, 2020.
A. Celani and P. Giudici, “Endemic – epidemic models to understand COVID-19 spatio-temporal evolution,†Spat. Stat., no. xxxx, p. 100528, 2021.
C. Gu and A. Kurov, “Informational Role of Social Media: Evidence from Twitter Sentiment Assistant,†Pre-Proof, 2020.
Y.-P. Lee, H. Tsai, and J.-S. Wu, “Exploring the Impact of User Personality and Self- Disclosure on the Continuous Use of Social Media,†J. Econ. Bus., vol. 3, no. 4, pp. 1324–1343, 2020.
I. F. Ramadhy and Y. Sibaroni, “Analisis Trending Topik Twitter dengan Fitur Ekspansi FastText Menggunakan Metode Logistic Regression,†J. Ris. Komputer), vol. 9, no. 1, pp. 2407–389, 2022.
D. Alita, Y. Fernando, and H. Sulistiani, “IMPLEMENTASI ALGORITMA MULTICLASS SVM PADA OPINI PUBLIK BERBAHASA INDONESIA DI TWITTER,†Teknokompak, vol. 14, no. 2, pp. 86–91, 2020.
I. Knezevic et al., “WHO International Standard for evaluation of the antibody response to COVID-19 vaccines : call for urgent action by the scientific community,†The Lancet Microbe, vol. 5247, no. 21, pp. 1–6, 2021.
N. F. Octarina, E. Kongres, and M. Mardika, “Urgensi Penemuan Vaksin Covid-19 sebagai Hak Milik Publik,†Pandecta Res. Law J., vol. 16, no. 1, pp. 106–119, 2021.
P. Lumbanraja, “Pengaruh Karakteristik Individu , Gaya Kepemimpinan dan Budaya Organisasi terhadap Kepuasan Kerja dan Komitmen Organisasi ( Studi pada Pemerintah Daerah di Provinsi Sumatera Utara ),†Dikti, vol. 7, no. NO. 43/DIKTI/KEP/2008 ISSN: 1693-5241, 2009.
J. Xue et al., “Twitter Discussions and Emotions About the COVID-19 Pandemic : Machine Learning Approach,†J. Med. INTERNET Res., vol. 22, no. 11, pp. 1–14, 2020.
Pristiyono, M. Ritonga, M. A. Al Ihsan, A. Anjar, and F. H. Rambe, “Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm,†in Annual Conference on Computer Science and Engineering Technology (AC2SET) 2020, 2021.
L. Nemes and A. Kiss, “Social media sentiment analysis based on COVID-19,†J. Inf. Telecommun., vol. 5, no. 1, pp. 1–15, 2021.
M. Hung et al., “Social Network Analysis of COVID-19 Sentiments : Application of Artificial Intelligence Corresponding Author :,†vol. 22, pp. 1–13, 2020.
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,†J. Komtika (Komputasi dan Inform., vol. 5, no. 1, pp. 19–25, 2021.
M. Lestandy, A. Abdurrahim, and L. Syafa’ah, “Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naive Bayes,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 10, pp. 802–808, 2021.
S. Saidah and J. Mayary, “ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP DOMPET ELEKTRONIK DENGAN METODE LEXICON BASED DAN K – NEAREST NEIGHBOR,†J. Ilm. Inform. Komput., pp. 1–17, 2020.
A. D. A. Putra and S. Juanita, “Analisis Sentimen Pada Ulasan Pengguna Aplikasi Bibit Dan Bareksa Dengan Algoritma KNN,†J. Tek. Inform. dan Sist. Inf., vol. 8, no. 2, pp. 636–646, 2021.
F. M. J. M. Shamrat, P. Ghosh, M. H. Sadek, M. A. Kazi, and T. M. Shitab, “Implementation of Machine Learning Algorithms to Detect the Prognosis Rate of Kidney Disease Implementation of Machine Learning Algorithms to Detect the Prognosis Rate of Kidney Disease,†Accel. ing world’s Res., 2020.
P. Ghosh et al., “Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques,†IEEE Access, vol. 9, 2021.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).