SENTIMENT ANALYSIS OF INDONESIAN COMMUNITY ON COVID-19 VACCINATION ON TWITTER SOCIAL MEDIA

  • Nurmalasari Nurmalasari (1*) STMIK Nusa Mandiri
  • Widi Astuti (2) Universitas Nusa Mandiri
  • Windu Gata (3) Universitas Nusa Mandiri
  • Ida Zuniarti (4) Universitas Nusa Mandiri

  • (*) Corresponding Author
Keywords: Data Mining, Covid-19 Vaccine, Twitter, Naive Bayes, SVM, Logistic Regression

Abstract

In the process, data mining will extract valuable information by analyzing the existence of specific patterns or relationships from extensive data. One of the concerns of the new disease outbreak caused by the coronavirus (2019-nCoV) or commonly referred to as Covid-19, was officially designated as a global pandemic by the World Health Organization (WFO) on March 11, 2020. To break the transmission of Covid-19, the government carried out vaccinations for the Indonesian population. In the first period, the vaccination target will be for health workers with a total of 1.3 million people, public officers with 17.4 million people, and 21.5 million people. 19. The Data processed is only text data from Twitter application reviews that use Indonesian. Using the polarity of the Sentiment class Textblob, the sentiment class is positive, negative, and neutral. The data mining used is SVM, Naive Bayes, and Logistic Regression. As for this research in the form of knowledge of sentiment in the community towards vaccination activities, the results of this study get 43% positive sentiment, 40.8% negative, and 16.2% negative by testing the classification algorithm, Logistic Regression accuracy of 87%, SVM 86, 4%, and Naive Bayes, 40% of these results, can be seen that the Indonesian people have a positive sentiment towards the covid-19 vaccine.

Downloads

Download data is not yet available.

References

Ahuja, S., & Dubey, G. (2017). Clustering and Sentiment Analysis on Twitter Data. 2017 2nd International Conference on Telecommunication and Networks (TEL-NET), 1–5(1), 1–5. https://doi.org/10.1109/TEL-NET.2017.8343568

Dewi, S. A. E. (2021). Komunikasi Publik Terkait Vaksinasi Covid 19. Health Care : Jurnal Kesehatan, 10(1), 162–167. https://doi.org/10.36763/healthcare.v10i1.119

Ian H. Witten, Eibe Frank, Mark A. Hall, C. J. P. (2019). Data Mining Practical Machine Learning Tools and Techniques. (C. Kent, Ed.). Todd Green. Retrieved from https://www.sciencedirect.com/book/9780123748560/data-mining-practical-machine-learning-tools-and-techniques#book-description

Ilmiah, P., Afshoh, F., Informatika, P. S., Komunikasi, F., Informatika, D. A. N., & Surakarta, U. M. (2018). Analisa Sentimen Menggunakan Naïve Bayes. Jurnal Sains Dan Teknologi, 10(2), 2. https://doi.org/https://doi.org/10.32764/saintekbu.v10i2.190

K-means, M. A. (2017). Text Mining Untuk Analisis Sentimen Review Film. Techno.COM, 16(1), 1–8. https://doi.org/10.33633/tc.v16i1.1263

Keahlian, K., & Data, R. (2021). Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial, 1(1), 10–12. https://doi.org/https://doi.org/10.20895/dinda.v1i1.180

Kurniawan, S., Gata, W., Puspitawati, D. A., Parthama, I. K. S., Setiawan, H., S, A., & Hartini. (2019). Text Mining Pre-Processing Using Gata Framework and RapidMiner for Indonesian Sentiment Analysis Text Mining Pre-Processing Using Gata Framework and RapidMiner for Indonesian Sentiment Analysis. IOP Conference Series: Materials Science and Engineering, 385(1), 1. https://doi.org/10.1088/1757-899X/835/1/012057

Makmun, A., & Hazhiyah, S. F. (2020). Kajian Pustaka Tinjauan Terkait Pengembangan Vaksin Covid – 19 Fakultas Kedokteran Universitas Muslim Indonesia. Molucca Media, 13(oktober), 2. https://doi.org/https://doi.org/10.30598/molmed.2020.v13.i2.52

Mart, F., Contreras-ochando, L., & Lachiche, N. (2019). CRISP-DM Twenty Years Later : From Data Mining Processes to Data Science Trajectories. IEEE Xplore, 33(8), 1. https://doi.org/10.1109/TKDE.2019.2962680

Muttaqien, D. D., Tibyani, T., & Hartono, P. P. (2022). Implementasi Support Vector Machine pada Analisis Sentimen mengenai Bantuan Sosial di Era Pandemi Covid-19 pada Pengguna Twitter. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 10(1), 6. https://doi.org/http://j - ptiik.ub.ac.id 2548 - 964X

Nur Khormarudin, A. (2016). Teknik Data Mining: Algoritma K-Means Clustering. Jurnal Ilmu Komputer, 1(1), 1–12. Retrieved from https://ilmukomputer.org/category/datamining/

Primadhita, Y., & Budiningsih, S. (2020). Analisis Perkembangan Usaha Mikro Kecil Dan Menengah Dengan Model Vector Auto Regression. Jurnal Manajemen Kewirausahaan, 17(1), 1. https://doi.org/10.33370/jmk.v17i1.396

Rachman, F. F., & Pramana, S. (2020). Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter, 8(2), 100–109. https://doi.org/https://doi.org/10.47007/inohim.v8i2.223

Susilo Daniel, P. D. T., & Navarro, J. C. (2021). Performance of Indonesian Ministry of Health in Overcoming Hoax About Vaccination Amid the Covid-19 Pandemic on Social Media. NYIMAK Journal of Communication, 5(1), 1–66. https://doi.org/https://2580-3808

Yolanda, I. (2021). Urgensi Pengaturan Trading In Influence Sebagai Sarana Pembangunan Masyarakat. DiH: Jurnal Ilmu Hukum, 6534(17), 1. https://doi.org/https://doi.org/10.30996/dih.v17i1.4132

Published
2022-09-13
How to Cite
Nurmalasari, N., Astuti, W., Gata, W., & Zuniarti, I. (2022). SENTIMENT ANALYSIS OF INDONESIAN COMMUNITY ON COVID-19 VACCINATION ON TWITTER SOCIAL MEDIA. Jurnal Pilar Nusa Mandiri, 18(2), 161-166. https://doi.org/10.33480/pilar.v18i2.3820
Article Metrics

Abstract viewed = 62 times
PDF downloaded = 49 times

Most read articles by the same author(s)