COVID-19 Vaccination and PPKM Policy with the Implementation of the Fuzzy Sugeno Method to Income Classification

Authors

  • Djihan Wahyuni Departement of Statistics, Universitas Brawijaya, 65145, Malang
  • Eni Sumarminingsih Departement of Statistics, Universitas Brawijaya, 65145, Malang
  • Suci Astutik Departement of Statistics, Universitas Brawijaya, 65145, Malang

DOI:

https://doi.org/10.31764/jtam.v6i4.10096

Keywords:

Sentiment Analysis, FIS Sugeno, PPKM, Covid-19 Vaccination.

Abstract

This study aims to determine the implementation of Fuzzy Sugeno in classifying textual data obtained from Twitter so as to determine the polarity of public opinion regarding PPKM policies and Covid-19 vaccinations. This study uses primary data via Twitter related to COVID-19 vaccination and PPKM policies in Indonesia starting from February 9, 2021 to January 17, 2022. There are several stages carried out, namely data collection, data pre-processing, data labeling, data weighting. , identification of membership functions, determination of fuzzy sets, formation of classification systems, and evaluation of classification results. The results of this study explain that Fuzzy Sugeno's performance in classifying tweets is quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding PPKM policies and Covid-19 vaccinations tends to be balanced with 36.92% of tweets classified as positive sentiments, 22.85% negative sentiments, and another 40.23% classified as neutral sentiments. In addition, the fuzzy set that is formed based on the data observation method is very well done because it is able to adjust the frequency of the data in each category. This really helps improve the performance of the built classification system.

 

References

Aryandani, A., Solimun, Nurjannah, Rinaldo Fernandes, A.A. & Efendi, A. (2022). Implementation of Fuzzy C-Means in Investor Group in the Stock Market Post-Covid-19 Pandemic. WSEAS Transactions on Mathematics, 21, 415–423.

Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.

Civelek, Z. (2020). Optimization of fuzzy logic (Takagi-Sugeno) blade pitch angle controller in wind turbines by genetic algorithm. Engineering Science and Technology, an International Journal, 23(1), 1–9.

Djalante, R., Lassa, J., Setiamarga, D., Sudjatma, A., Indrawan, M., Haryanto, B., ... & Warsilah, H. (2020). Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March 2020. Progress in Disaster Science, 6, 100091.

Efrilianda, D., Dianti, E., & Khoirunnisa, O. (2021). Analysis of Twitter Sentiment in Covid-19 Era Using Fuzzy Logic Method. Journal of Soft Computing Exploration, 1–5.

Fu, G., & Wang, X. (2010). Chinese Sentence-Level Sentiment Classification Based on Fuzzy Sets. Coling, 312–319.

Gorunescu, F. (2011). Data Mining: Concepts, Model, and Techniques. Springer.

Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques third edition. Morgan Kaufmann Publisher.

Hu, Z., Wang, J., Zhang, C., Luo, Z., Luo, X., Xiao, L., & Shi, J. (2021). Uncertainty modeling for multi center autism spectrum disorder classification using takagi-sugeno-kang fuzzy systems. IEEE Transactions on Cognitive and Developmental Systems.

Iwendi, C., Mahboob, K., Khalid, Z., Javed, A. R., Rizwan, M., & Ghosh, U. (2022). Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system. Multimedia Systems, 28(4), 1223–1237.

Kaur, A. (2012). Comparison of Fizzy Logic and Neuro Fuzzy Algorithms for Air Conditioning System. International Journal of Soft Computing and Engineering, 417–420.

Kumar, K., & Prakash, A. (2019). Developing a framework for assessing sustainable banking performance of the Indian banking sector. Social Responsibility Journal.

Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan and Claypool Publishers.

MathWorks. (2014). Fuzzy Logic Toolbox User’s Guide. The MathWorks, Inc.

Paroubek, P., & Pak, A. (2010). Twitter as a Corpus for Sentiment Analysis and Opinion Mining. Seventh International Conference on Language Resource and Evaluation.

Pedersen, Y. Y. (1997). A comparative study on feature selection in text categorization. Proc of ICML-97, 412–420.

Rauf, H. T., Malik, S., Shoaib, U., Irfan, M. N., & Lali, M. I. (2020). Adaptive inertia weight Bat algorithm with Sugeno-Function fuzzy search. Applied Soft Computing, 90, 106159.

Şahin, M. (2021). A comprehensive analysis of weighting and multicriteria methods in the context of sustainable energy. International Journal of Environmental Science and Technology, 18(6), 1591–1616.

Serrano-Guerrero, J., Romero, F., Olivas, J. (2021). Fuzzy logic Applied to Opinion Mining: A Review. Journal of Knowledge-Based Systems.

Setiawan, H., Utami, E., & S. (2021). Twitter Sentiment Analysis Post-Covid-19 Online Lecture Using Support Vector Machine and Naive Bayes Algorithm. Journal of Computing and Informatics, 43–51.

T Thakkar, H., Shah, V., Yagnik, H., & Shah, M. (2021). Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis. Clinical EHealth, 4, 12–23.

Thangaraj, M., & Sivakami, M. (2018). Text Classification Techniques: A Literature Review. Interdisciplinary Journal of Information, Knowledge, and Management (IJIKM), 118–134.

Torkayesh, A. E., Ecer, F., Pamucar, D., & Karamaşa, Ç. (2021). Comparative assessment of social sustainability performance: Integrated data-driven weighting system and CoCoSo model. Sustainable Cities and Society, 71, 102975.

Twitter. (2021). https://about.twitter.com/.

Villavicencio, C., Macrohon, J., Inbaraj, X., Jeng, J., & Hsieh, J. (2021). Twitter Sentiment Analysis towards Covid-19 Vaccines in the Phileppines Using Naïve Bayes. Journal of Information.

Wang, L. L., & Lo, K. (2021). Text mining approaches for dealing with the rapidly expanding literature on COVID-19. Briefings in Bioinformatics, 22(2), 781–799.

Wang, S., Celebi, M. E., Zhang, Y. D., Yu, X., Lu, S., Yao, X., ... & Tyukin, I. (2021). Advances in data preprocessing for biomedical data fusion: an overview of the methods, challenges, and prospects. Information Fusion, 76, 376–421.

Xia, E., Yue, H., & Liu, H. (2021). Tweet sentiment analysis of the 2020 US presidential election. Companion Proceedings of the Web Conference 2021, 367–371.

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Published

2022-10-07

Issue

Vol. 6 No. 4 (2022): October

Section

Articles

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