ANALYSIS CLASSIFICATION SENTIMENT OF THE LARGE PRIEST OF FPI’S RETURN USING SVM CLASSIFICATION WITH OVERSAMPLING METHOD

Zetta Nillawati Reyka Putri; Muhammad Muhajir

doi:10.34288/jri.v4i1.132

Authors

Zetta Nillawati Reyka Putri Universitas Islam Indonesia
Muhammad Muhajir Universitas Islam Indonesia

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v4i1.132

Keywords:

Habib Rizieq, Support Vector Machine, Text Association, Text Mining, Twitter

Abstract

At the end of 2020, Habib Rizieq's return to Indonesia drew criticism from the public for causing crowds during the Covid-19 pandemic. News and opinions about Habib Rizieq fill internet platforms, including Twitter. The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage. The SVM classification of unbalanced data between positive and negative classes resulted in 95.06% accuracy with a negative class precision value of 84% and better than 72% recall, in the positive class the precision value was 96% less than 2% of recall 98%. While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. The results of positive sentiments are known that the public will always support and want freedom for Rizieq, for negative sentiments it is known that many people are disappointed with Rizieq regarding the lies of his swab test results.

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References

Cai, J., Luo, J., Wang, S., & Yang, S. (2018). Feature selection in machine learning: A new perspective. Neurocomputing, 300, 70–79. https://doi.org/10.1016/j.neucom.2017.11.077

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16(June), 321–357. https://doi.org/10.1613/jair.953

He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the International Joint Conference on Neural Networks, 3, 1322–1328. https://doi.org/10.1109/IJCNN.2008.4633969

Indra, P. A. (2021). Muhammad Rizieq Shihab. In Viva.Co.Id. https://en.wikipedia.org/wiki/Muhammad_Rizieq_Shihab

Jangid, B. M., Jadhav, C. K., Dhokate, S. M., & , Grish M.Jadhav, P. G. M. B. (2016). Proposed Stemming Algorithm for Hindi Information Retrieval. International Journal of Innovative Research in Computer and Communication Engineering (An ISO Certified Organization), 3297(6), 11449–11455. https://doi.org/10.15680/IJIRCCE.2016

Kannan, S., & Gurusamy, V. (2014). Preprocessing Techniques for Text Mining (PDF Download Available). October 2014.

Manning, C. D. (2009). Introduction to Modern Information Retrieval (2nd edition). Library Review, 53(9), 462–463. https://doi.org/10.1108/00242530410565256

Qurtuby, S. al. (2018). sumanto. https://www.dw.com/id/mayoritas-publik-menolak-rizieq-shihab-kembali-ke-indonesia/a-46573605

Riyanto, A. D. (2020). Indonesia Digital report 2020. Global Digital Insights, 43. https://datareportal.com/reports/digital-2020-indonesia?rq=digital-2020-indonesia

Saputra, A. R. I. (2018). Ari saputra (p. 2595). https://news.detik.com/berita/d-5328884/3-status-tersangka-habib-rizieq-di-3-kasus

V. Vapnik, C. C. (1995). Photonic neural networks and learning machines the role of electron-trapping materials. IEEE Expert-Intelligent Systems and Their Applications, 7(5), 63–72. https://doi.org/10.1109/64.163674

Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988–999. https://doi.org/10.1109/72.788640

Yang, Z. M., He, J. Y., & Shao, Y. H. (2013). Feature selection based on linear twin support vector machines. Procedia Computer Science, 17, 1039–1046. https://doi.org/10.1016/j.procs.2013.05.132