Sentiment Analysis of Vaccine Booster during Covid-19: Indonesian Netizen Perspective Based on Twitter Dataset

Khoiril Hikmah; Abd. Charis Fauzan; Harliana Harliana

doi:10.56327/jtksi.v5i2.1161

Sentiment Analysis of Vaccine Booster during Covid-19: Indonesian Netizen Perspective Based on Twitter Dataset

Khoiril Hikmah⁽¹⁾, Abd. Charis Fauzan⁽²⁾, Harliana Harliana⁽³⁾,

(1) Program Studi Ilmu Komputer, Universitas Nahdlatul Ulama Blitar, Jawa Timur
(2) Program Studi Ilmu Komputer, Universitas Nahdlatul Ulama Blitar, Jawa Timur
(3) Program Studi Ilmu Komputer, Universitas Nahdlatul Ulama Blitar, Jawa Timur

Corresponding Author

Abstract

Corona virus has become a global threat at the end of 2019. The spread of this corona virus is very fast to all countries in the world. The World Health Organization (WHO) has determined the status of the corona virus as a global pandemic called the Corona Virus Desease 2019 (Covid-19). Indonesia was detected first case of Covid-19 on March 2, 2020. After that, the number of Covid-19 cases in Indonesia increased every day and had a real impact on various sectors sectors, such as the economic and education. The Indonesian government has handled this health disaster, one of way that has been done is by holding a COVID-19 vaccine. Includes one dose vaccine, second dose vaccine and vaccine booster. The existence of this vaccine booster has received various opinions from Indonesian netizens who were conveyed through social media of Twitter. Therefore, this research aims to analyze the sentiments of Indonesian netizens about booster vaccination. In this study, data was collected from the Twitter dataset by crawling using the Rapidminer. Then, the data preprocessing stage is carried out consisting of: case folding, tokenizing, filtering and stemming. Sentiment classification is divided into positive sentiment, negative sentiment and neutral sentiment. Sentiment classification resolved using the Naive Bayes algorithm. This research resulted sentiment of vaccine booster during Covid-19 based on Indonesian netizen, include tweets of 23% positive sentiment, tweets of 15% neutral sentiment and tweets of 76% negative sentiment with an accuracy rate of 89%.

Keywords

Twitter; Vaccine Booster; Naive Bayes; Sentiment Analysis

References

J. S. Asri and S. Wahyu, “Analisis Sentimen Menerapkan Lexicon-Learning Based Untuk Melihat Opini Masyarakat Mengenai Protokol Kesehatan Dan Perkembangan Vaksin Covid-19 Di Indonesia Menggunakan Dataset Twitter,” Konf. Nas. Ilmu Komput., pp. 530–536, 2021.

B. Laurensz and Eko Sediyono, “Analisis Sentimen Masyarakat terhadap Tindakan Vaksinasi dalam Upaya Mengatasi Pandemi Covid-19,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 10, no. 2, pp. 118–123, 2021, doi: 10.22146/jnteti.v10i2.1421.

E. Nurhazizah, R. N. Ichsan, and S. Widiyanesti, “Analisis Sentimen Dan Jaringan Sosial Pada Penyebaran Informasi Vaksinasi Di Twitter,” vol. 10, no. 1, pp. 24–35, 2022.

F. F. Rachman and S. Pramana, “Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter,” vol. 8, no. 2, pp. 100–109, 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,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1088, no. 1, p. 012045, 2021, doi: 10.1088/1757-899x/1088/1/012045.

K. K. Agustiningsih, E. Utami, O. Muhammad, and A. Alsyaibani, “Sentiment Analysis of COVID-19 Vaccines in Indonesia on Twitter Using Pre-Trained and Self-Training Word Embeddings,” vol. 1, pp. 39–46, 2022.

S. Lestari and S. Saepudin, “ANALISIS SENTIMEN VAKSIN SINOVAC PADA TWITTER MENGGUNAKAN ALGORITMA NAIVE BAYES,” 2021.

F. Septianingrum, J. H. Jaman, and U. Enri, “Analisis Sentimen Pada Isu Vaksin Covid-19 di Indonesia dengan Metode Naive Bayes Classifier,” vol. 5, pp. 1431–1437, 2021, doi: 10.30865/mib.v5i4.3260.

I. W. D. Gafatia and N. Hadinata, “Analisis Pro Kontra Vaksin Covid 19 Menggunakan Sentiment Analysis Sumber Media Sosial Twitter,” J. Pengemb. Sist. Inf. dan Inform., vol. 2, no. 1, pp. 34–42, 2021, doi: 10.47747/jpsii.v2i1.544.

M. I. Aditama, R. I. Pratama, K. H. U. Wiwaha, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Pengguna Media Sosial Twitter Terhadap Pengadaan Vaksin COVID-19,” J. Inf. Eng. Educ. Technol., vol. 4, no. 2, pp. 90–92, 2020.

C.- Di Twitter, F. A. Hasanuddin, M. Akbar, and M. Farid, “Analisis Kluster Terpaan Selektif Dalam Jaringan Informasi Vaksin,” vol. 08, no. January, pp. 713–732, 2022.

E. Prabowo and A. C. Fauzan, “Implementasi Algoritma Naive Bayes Classifier Untuk Penentuan Kelayakan Penerima Beasiswa Peningkatan Prestasi Akademik (PPA) Bagi Mahasiswa,” Ilk. J. Comput. Sci. Appl. Informatics, vol. 3, no. 2, pp. 165–177, 2021, doi: 10.28926/ilkomnika.v3i2.296.

J. Jtik, J. Teknologi, S. H. Ramadhani, and M. I. Wahyudin, “Analisis Sentimen Terhadap Vaksinasi Astra Zeneca pada Twitter Menggunakan Metode Naïve Bayes dan K-NN,” vol. 6, no. 4, 2022.

A. C. Fauzan and S. D. Purwanto, “Analisis Perbandingan Algoritma Naive Bayes Classifier dan Learning Vector Quantization dalam Sistem Identifikasi Boraks pada Bakso Daging Sapi,” vol. 7, no. 2, pp. 43–50, 2021, doi: 10.24014/coreit.v7i2.15085.

Full Text: PDF