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Feb 10, 2023
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Mar 13, 2023
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Mar 13, 2023
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Abstract

The outbreak of the COVID-19 virus that attacked the country of Indonesia made the government implement a policy, namely giving vaccinations. Since the announcement of the government's policy on administering the COVID-19 vaccine in January 2021, there have been a lot of discussions, especially on social media. One of the social media that is widely used by the public for opinions is Twitter. this has resulted in Twitter becoming a medium for expressing people's thoughts on administering the COVID-19 vaccine. Opinions generated can be positive, negative, or neutral towards vaccine administration. Based on the description of the problem, the study aims to analyze the sentiments of netizens regarding government policies in administering vaccinations. The method used is the KNN Algorithm to carry out sentiment analysis through public opinion on Twitter social media. The results sentiment with the highest value is positive which has more than 150 sentences. Then it produces an accuracy of 86.6% Precision of 85% and Recalls of 81%.

Keywords

Covid-19, Vaccination Twitter

Article Details

Creative Commons License

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

How to Cite
Aryanto, F., Fauzi, A., Fitri Nur Masruriyah, A. ., Lia Hananto, A., & Darmansyah. (2023). Sentiment Analysis Of Vaccination Using The K-Nearest Neighbor Algorithm. Edutran Computer Science and Information Technology, 1(1), 34-41. https://doi.org/10.59805/ecsit.v1i1.6

References

  1. [1] A. Salma and W. Silfianti, “Sentiment Analysis of User Reviews on COVID-19 Information Applications Using Naive Bayes Classifier , Support Vector Machine , and K-Nearest Neighbor,” vol. 6, no. 4, pp. 158–162, 2021.
  2. [2] C. Author and P. Gupta, “Pr ep rin t n ot pe er r ie we d Pr ep rin t n ot pe er we”.
  3. [3 ]A. Winanto and C. Budihartanti, “Comparison of the Accuracy of Sentiment Analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time,” vol. 3, no. 1, pp. 14–27, 2022.
  4. [4] C. Villavicencio, J. J. Macrohon, X. A. Inbaraj, J. Jeng, and J. Hsieh, “Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes,” 2021.
  5. [5] A. Baines, M. Ittefaq, and M. Abwao, “#Scamdemic, #plandemic, or #scaredemic: What parler social media platform tells us about covid‐19 vaccine,” Vaccines, vol. 9, no. 5, 2021, doi: 10.3390/vaccines9050421.
  6. [6] A. A. Chaudhri, S. S. Saranya, and S. Dubey, “Implementation Paper on Analyzing COVID-19 Vaccines on Twitter Dataset Using Tweepy and Text Blob,” Ann. R.S.C.B., vol. 25, no. 3, pp. 8393–8396, 2021.
  7. [7] J. French, S. Deshpande, W. Evans, and R. Obregon, “Key guidelines in developing a pre-emptive COVID-19 vaccination uptake promotion strategy,” Int. J. Environ. Res. Public Health, vol. 17, no. 16, pp. 1–14, 2020, doi: 10.3390/ijerph17165893.
  8. [8] N. N. Alabid and Z. D. Katheeth, “Sentiment analysis of twitter posts related to the covid-19 vaccines,” Indones. J. Electr. Eng. Comput. Sci., vol. 24, no. 3, pp. 1727–1734, 2021, doi: 10.11591/ijeecs.v24.i3.pp1727-1734.
  9. [9] A. Shahzad et al., “COVID-19 Vaccines Related User’s Response Categorization Using Machine Learning Techniques,” Computation, vol. 10, no. 8, pp. 1–29, 2022, doi: 10.3390/computation10080141.
  10. [10 ]A. M. H. Van Der Veen and M. G. Cox, “Getting started with uncertainty evaluation using the Monte Carlo method in R,” Accredit. Qual. Assur., vol. 26, no. 3, pp. 129–141, 2021, doi: 10.1007/s00769-021-01469-5.
  11. [11] S. Vijayarani, M. J. Ilamathi, and M. Nithya, “Preprocessing Techniques for Text Mining - An Overview Preprocessing Techniques for Text Mining - An Overview,” no. February, 2020.
  12. [12] E. Hossain and M. A. Rahaman, “A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier,” pp. 7–9, 2019.
  13. [13] J. Li, W. Chang, and Q. Li, “Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm,” 2021, doi: 10.1088/1757-899X/1088/1/012045.
  14. [14] F. M. J. M. Shamrat, S. Chakraborty, M. M. Imran, and J. N. Muna, “Sentiment analysis on twitter tweets about COVID-19 vaccines using NLP and supervised KNN classification algorithm,” vol. 23, no. 1, pp. 463–470, 2021, doi: 10.11591/ijeecs.v23.i1.pp463-470.
  15. [15] S. Mishra, A. Verma, K. Meena, and R. Kaushal, “Public reactions towards Covid-19 vaccination through twitter before and after second wave in India,” Soc. Netw. Anal. Min., vol. 12, no. 1, pp. 1–16, 2022, doi: 10.1007/s13278-022-00885-w.
  16. [16] J. O. Atoum and M. Nouman, “Sentiment Analysis of Arabic Jordanian Dialect Tweets,” vol. 10, no. 2, pp. 256–262, 2019.
  17. [17] H. Christian, M. P. Agus, and D. Suhartono, “SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY ( TF-IDF ),” pp. 285–294.
  18. [18] A. R. Lubis and M. Lubis, “Optimization of distance formula in K-Nearest Neighbor method,” vol. 9, no. 1, pp. 326–338, 2020, doi: 10.11591/eei.v9i1.1464.
  19. [19] K. C. Mitra and S. M. M. Islam, “Soil Classification using Machine Learning Methods and Crop Suggestion Based on Soil Series,” no. December 2018, 2020, doi: 10.1109/ICCITECHN.2018.8631943.
  20. [20] N. Saleena, “ScienceDirect An Ensemble Classification System for Twitter Sentiment Analysis An Ensemble Classification System for Twitter Sentiment Analysis,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 937–946, 2018, doi: 10.1016/j.procs.2018.05.109.