Comparative Analysis of Community Sentiment Against the Implementation of Booster Vaccination in Indonesia Using the K-Nearest Neighbor and Naïve Bayes Classifier Methods

Budiman Budiman, Wulandari Wulandari, Chairul Habibi


Sentiment analysis is a person's opinion or view of a particular object that produces positive, negative, or neutral sentiments. The government's effort during the COVID-19 pandemic is to call for the implementation of a booster vaccination program to the public. Based on this, it produces several public sentiments, some of which are uploaded on the Twitter social media platform, which generate positive and negative sentiments. To find out the classification of public sentiment, the researchers carried out calculations using the K-Nearest Neighbor and Naïve Bayes Classifier methods. Based on the calculation results, it was found that the public sentiment was positive at 98% and negative at 2%. This means that the community is enthusiastic and supports the booster vaccination program. Then the comparison based on the calculation results, namely the K-Nearest Neighbor method with a K value of 3 resulting in an accuracy calculation of 97.33% and using the Naïve Bayes Classifier method, an accuracy calculation of 97.35% can be generated. So it can be seen that using the Naïve Bayes Classifier method has a higher accuracy than the K-Nearest Neighbor method. 


Comparative analysis, booster vaccination, K-Nearest neighbor.

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Bentley, J. (1984). Programming pearls: algorithm design techniques. Communications of the ACM, 27(9), 865-873.

Hirji, K. K. (2001). Exploring data mining implementation. Communications of the ACM, 44(7), 87-93.

Islam, S., Ab Ghani, N., & Ahmed, M. (2020). A review on recent advances in Deep learning for Sentiment Analysis: Performances, Challenges and Limitations. Compusoft, 9(7), 3775-3783.

Million, M., Jarrot, P. A., Camoin-Jau, L., Colson, P., Fenollar, F., ... & Raoult, D. (2020). Natural history of COVID-19 and therapeutic options. Expert review of clinical immunology, 16(12), 1159-1184.

Rahardi, M., Aminuddin, A., Abdulloh, F. F., & Nugroho, R. A. (2022). Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia. International Journal of Advanced Computer Science and Applications, 13(6).

Skeels, M. M., & Grudin, J. (2009, May). When social networks cross boundaries: a case study of workplace use of facebook and linkedin. In Proceedings of the 2009 ACM International Conference on Supporting Group Work, 95-104.

Sugiyono, S. (2010). Educational Research Methods: Quantitative, Qualitative, and R & D Approaches. Bandung: CV. Alfabeta.

Thanaki, J. (2018). Machine Learning Solutions: Expert Techniques to Tackle Complex Machine Learning Problems Using Python. Britania Raya: Packt Publishing.

Tomlinson, B. (Ed.). (2011). Materials development in language teaching. Cambridge University Press.



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