Analisis Pro Kontra Vaksin Covid 19 Menggunakan Sentiment Analysis Sumber Media Sosial Twitter

Authors

  • I Wayan Desta Gafatia Universitas Bina Darma
  • Novri Hadinata Universitas Bina Darma

DOI:

https://doi.org/10.47747/jpsii.v2i1.544

Abstract

The development of information technology today has experienced very rapid growth. One of the developments in information technology, namely social media such as Twitter, Facebook, and Youtube, are some of the most popular communication media in today's society. Twitter is often used to express emotions about something, either praising or criticizing in the form of emotion. Human emotions can be categorized into five basic emotions, namely love, joy, sadness, anger, and fear. Twitter users' emotional tweets can be known as opinion or sentiment analysis (opinion analysis or sentiment analysis). Sentiment analysis is also carried out to see opinions or tendencies towards a problem or policy, whether they tend to have negative or positive opinions. The COVID-19 vaccine has become one of the discussions with a fairly high intensity on social media. Vaccine-related tweets have increased as government policies evolve. The responses of netizens also varied, ranging from clinical trials of vaccines, free vaccines, vaccine effectiveness, halal vaccines, to the implementation of vaccinations. This research produces a system that can analyze tweet sentiment related to the covid 19 vaccine in Indonesia where the tweet is obtained using the Twitter API. This system uses the Multinominal Naive Bayes method for the classification process.

References

Antoni, D., & Akbar, M. (2019). E-supply chain management value concept for the palm oil industry. Jurnal Sistem Informasi, 15(2), 15-29.

Antoni, D., Fikari, D., & Akbar, M. (2018). The readiness of palm oil industry in enterprise resource planning. Telkomnika, 16(6), 2692-2702.

Antoni, D., Herdiansyah, M. I., Akbar, M., & Sumitro, A. (2021). Pengembangan Infrastruktur Jaringan Untuk Meningkatkan Pelayanan Publik di Kota Palembang. JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(4), 1652-1659.

Antoni, D., Jie, F., & Abareshi, A. (2020). Critical factors in information technology capability for enhancing firm's environmental performance: case of Indonesian ICT sector. International Journal of Agile Systems and Management, 13(2), 159-181.

Bustami. 2014. Penerapan Algoritma Naïve Bayes untuk Mengklasifikasi Data nasabah Asuransi. Jurnal informatika Vol. 8, no. 1. Aceh Indonesia

Ebi, M., Yesi Novaria, K., and Ria, A. Eskplorasi trending topik twitter menggunakan text mining. SENTIKOM 2017.

Fauzi, F., Dencik, A. B., & Asiati, D. I. (2019). Metodologi Penelitian untuk manajemen dan akuntansi. Jakarta: Salemba Empat.

Masykur, F. (2014). Implementasi sistem informasi geografis menggunakan google maps api dalam pemetaan asal mahasiswa. Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 5(2):181–186.

Rizal, (2017). Analisis sentimen tentang opini pilkada DKI 2017 tentang dokumen Twitter berbahasa indonesia menggunakan naïve bayes dan pembobotan emoji. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer vol. 1, no. 12, desember 2017, pages 1718-1724

Sembodo, J. E., Setiawan, E. B., and Baizal, Z. A. (2016). Data crawling otomatis pada twitter. In Indonesian Symposium on Computing (Indo-SC), pages 11–16.

Simanullang, J. W., Adiwijaya, A., and Al Faraby, S. (2017). Klasifikasi sentimen pada movie review dengan metode multinomial nave bayes. ëProceedings of Engineering, 4(2).

Yustiani and Yunanto, 2017. Peran market Place alternatif Bisnis di era Teknologi Informasi. Jurnal ilmiah komputer dan informatika (KOMPUTA) vol. 6, no. 2, Oktober 2017

Downloads

Published

2021-11-23

How to Cite

Gafatia, I. W. D., & Hadinata, N. (2021). Analisis Pro Kontra Vaksin Covid 19 Menggunakan Sentiment Analysis Sumber Media Sosial Twitter. Jurnal Pengembangan Sistem Informasi Dan Informatika, 2(1), 34-42. https://doi.org/10.47747/jpsii.v2i1.544