Analysis of public opinion on COVID-19 vaccine through social media using Naïve Bayes theory algorithm


Aishiyah Saputri Laswi(1*); Munir Yusuf(2); Ulvah Ulvah(3); Bungawati Bungawati(4);

(1) Universitas Andi Djemma Palopo
(2) Institut Agama Islam Neger (IAIN) Palopo
(3) Universitas Cokroaminoto Palopo
(4) Institut Agama Islam Neger (IAIN) Palopo
(*) Corresponding Author

  

Abstract


This study aims to analyze various public opinion on the Covid-19 vaccines that appear on social media pages, especially on Facebook and Twitter via # (hastag). The death rate caused by COVID-19 was so high which reached 144,227 people until 2022. The Indonesian government required vaccines for the community starting from children aged 6 years as an effort to prevent the spread of the Covid-19 virus. Unfortunately, the implementation of complete vaccines in Indonesia has only reached 51.3% of the mandatory vaccine population, which is 140 million out of 339 million people. The non-achievement of the target set by the government causes the need to conduct a sentiment analysis on vaccines in Indonesia through social media. Based on the sample data, from 1000 words obtained from 320 opinions there are positive and negative opinions. This data is then analyzed and processed to find out how many positive and negative responses occurred. The data was then processed into several stages to test the level of truth through training data and test data. The results of the data processing were tested using the Naïve Bayes algorithm which resulted in an accuracy value with a precession of 77.08% taken from 90 samples test data, recall with a percentage of 97.87% based on positive data which was predicted to be true with a positive opinion status from 47 samples of test data and 1 positive data status which is still predicted to be negative. Furthermore, the specific percentage value obtained was 65.30% of the 132 test data that are predicted to be true negative.


Keywords


Analysis; Opinion; Social Media; Naïve Bayes

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 424 times
PDF view: 152 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v14i2.1127.160-168 		  

Cite

How to cite item

References


A. Harun and D. P. Ananda, “Analysis of public opinion sentiment about COVID-19 vaccination in Indonesia using Naïve Bayes and Decission Tree Analisa Sentimen opini publik tentang vaksinasi COVID-19 di Indonesia menggunakan Naïve Bayes dan Decission Tree,” Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. April, pp. 58–63, 2021.

A. Baita, Y. Pristyanto, N. Cahyono, P. Covid-, K. N. N. Akurasi, and K. Kunci, “analisis sentimen mengenai vaksin sinovac menggunakan algoritma Support Vector Machine ( SVM ) dan K-Nearst Neighbor ( KNN ) abstraksi keywords :,” vol. 4, no. 2, pp. 42–46, 2021.

S. Lestari and S. Saepudin, “Analisis sentimen vaksin sinovac pada twitter menggunakan algoritma Naive Bayes,” SISMATIK (Seminar Nas. Sist. Inf. dan Manaj. Inform., pp. 163–170, 2021.

S. S. Aljameel et al., “A sentiment analysis approach to predict an individual’s awareness of the precautionary procedures to prevent covid-19 outbreaks in Saudi Arabia,” Int. J. Environ. Res. Public Health, vol. 18, no. 1, pp. 1–12, 2021, doi: 10.3390/ijerph18010218.

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.

D. Sandi, E. Utami, and A. Fatkhurohman, “Klasifikasi opini dengan menggunakan algoritma K- Nearest Neighbor pada berita vaksinasi di twitter,” vol. 16, 2022.

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, doi: 10.31603/komtika.v5i1.5185.

Merinda Lestandy, Abdurrahim Abdurrahim, and Lailis Syafa’ah, “Analisis sentimen tweet vaksin COVID-19 menggunakan Recurrent Neural Network dan Naïve Bayes,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 802–808, 2021, doi: 10.29207/resti.v5i4.3308.

President of the Republic of Indonesia, “Peraturan Presiden No. 99 Tahun 2020 tentang pengadaan vaksin dan pelaksanaan vaksinasi dalam rangka penanggulangan pandemi Corona Virus Disease 2019,” Pres. Regul., vol. 2019, no. 039471, pp. 1–13, 2020, [Online]. Available: https://peraturan.bpk.go.id/Home/Details/147944/perpres-no-99-tahun-2020.

Government of Indonesia, “Presidential Decree Number 99,” vol. 2019, no. 1, pp. 1–8, 2020.

W. Yulita et al., “Analisis sentimen terhadap opini masyarakat tentang vaksin COVID-19 menggunakan algoritma Naïve Bayes Classifier,” J. Data Min. dan Sist. Inf., vol. 2, no. 2, pp. 1–9, 2021, [Online]. Available: https://ejurnal.teknokrat.ac.id/index.php/JDMSI/article/view/1344.

P. Arsi, L. N. Hidayati, and A. Nurhakim, “Komparasi model klasifikasi sentimen issue vaksin COVID-19 berbasis platform instagram,” vol. 6, pp. 459–466, 2022, doi: 10.30865/mib.v6i1.3509.

A. Putra, D. Haeirudin, and H. Khairunnisa, “Analisis sentimen masyarakat terhadap kebijakan PPKM pada Media Sosial Twitter Menggunakan Algoritma Svm,” no. November, 2021.

P. D. Bangsa and I. Hermawan, “Jurnal Teknologi Terpadu,” J. Teknol. Terpadu, vol. 7, no. 1, pp. 15–22, 2021.

B. Bahekar, P. Gautam, and S. Sharma, “Literature review on sentiment analysis and opinion classifications on the impact of COVID19 outbreak,” vol. 9, no. 1, pp. 94–99, 2022.

S. Lorena., “Teknik data mining menggunakan metode Bayes Classifier untuk optimalisasi pencarian aplikasi perpustakaan,” J. Tek. Komput., vol. 4, no. 2, pp. 17–20, 2016.

R. E. Putri, Suparti, and R. Rahmawati, “Perbandingan metode klasifikasi Naãve Bayes dan K-Nearest Neighbor pada analisis data status kerja di kabupaten demak tahun 2012,” J. Gaussian, vol. 3, no. 4, pp. 831–838, 2014.

N. Imtiaz Khan, T. Mahmud, and M. Nazrul Islam, “COVID‐19 and black fungus: Analysis of the public perceptions through machine learning,” Eng. Reports, no. September, pp. 1–10, 2021, doi: 10.1002/eng2.12475.

E. S. Negara, R. Andryani, and P. H. Saksono, “Analisis data twitter: ekstraksi dan analisis data g eospasial,” J. INKOM, vol. 10, no. 1, p. 27, 2016, doi: 10.14203/j.inkom.433.

D. D. Palmer, “Text Pre-processing,” Handb. Nat. Lang. Process. Second Ed., 2010.

D. Rustiana and N. Rahayu, “Analisis sentimen pasar otomotif mobil: tweet twitter menggunakan Naïve Bayes,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 8, no. 1, pp. 113–120, 2017, doi: 10.24176/simet.v8i1.841.

L. Mutawalli, M. T. A. Zaen, and W. Bagye, “Klasifikasi teks sosial media twitter menggunakan Support Vector Machine (Studi Kasus Penusukan Wiranto),” J. Inform. dan Rekayasa Elektron., vol. 2, no. 2, p. 43, 2019, doi: 10.36595/jire.v2i2.117.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Aishiyah Saputri Laswi, Munir Yusuf, Ulvah Ulvah, Bungawati Bungawati

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.