Algoritma Support Vector Machine Untuk Analisis Sentimen Masyarakat Indonesia Terhadap Pandemi Virus Corona Di Media Sosial

Mhd. Furqan(1*), Mhd. Ikhsan(2), Rafizah Aini(3),

(1) Universitas Islam Negeri Sumatera Utara, Indonesia
(2) Universitas Islam Negeri Sumatera Utara, Indonesia
(3) Universitas Islam Negeri Sumatera Utara, Indonesia
(*) Corresponding Author

Abstract


The corona virus pandemic refers to the spread of coronavirus disease 2019 or what is known as coronavirus disease 2019 in various parts of the world. This outbreak is a change from a new type of coronavirus called SARS-CoV-2. The issue of this pandemic has become a hot topic of discussion, including on social media. The most frequently used platform among the public is Twitter. On social media, the corona virus pandemic has always been a topic of conversation that is often discussed, causing controversy. Controversy occurs because every day the opinions on social media Twitter regarding the corona virus pandemic are always increasing so that, when people read news on social media about the pandemic, it raises concerns because people's opinions are different. From this problem the author will create a system that analyzes opinions from Twitter social media to get opinion sentiment about what is happening in the community regarding the problem of the corona virus pandemic. This study uses the SVM method which is fast and effective for text classification. The results of this study will classify positive, negative and neutral sentences. The accuracy obtained from the model with the SVM algorithm is 98%. Testing is done by calculating precision, recall, F-measure..

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References


A. R. Lubis, S. Prayudani, M. Lubis, and O. Nugroho, “Sentiment Analysis on Online Learning During the Covid-19 Pandemic Based on Opinions on Twitter using KNN Method,” in 2022 1st International Conference on Information System & Information Technology (ICISIT), 2022, pp. 106–111.

A. R. Lubis, S. Prayudani, M. Lubis, and Al-Khowarizmi, “The Effect of E-Commerce Towards Sales Growth on Social Media among Students in Indonesia,” Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 2021-Octob, no. October, pp. 102–106, 2021, doi: 10.23919/EECSI53397.2021.9624290.

A. Susilo et al., “Coronavirus Disease 2019: Tinjauan Literatur Terkini,” J. Penyakit Dalam Indones., vol. 7, no. 1, p. 45, 2020, doi: 10.7454/jpdi.v7i1.415.

Y. Niar, K. Komariah, A. Surip, R. Saputra, and I. Ali, “Implementasi Algoritma Naïve Bayes Untuk Prediksi Persediaan Barang Rotan,” KOPERTIP J.

Ilm. Manaj. Inform. dan Komput., vol. 4, no. 1, pp. 28–34, 2022, doi:

32485/kopertip.v4i1.112.

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.

P. Sv et al., “Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People’s Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage,” Vaccines, vol. 10, no. 11, p. 1929, 2022.

J. Schüttler, R. Schlickeiser, F. Schlickeiser, and M. Kröger, “Covid-19 Predictions Using a Gauss Model, Based on Data from April 2,” Physics (College. Park. Md)., vol. 2, no. 2, pp. 197–212, 2020, doi: 10.3390/physics2020013.

J. M. Banda et al., “A large-scale COVID-19 Twitter chatter dataset for open scientific research—an international collaboration,” Epidemiologia, vol. 2, no. 3, pp. 315–324, 2021.

Y. Drias and H. Drias, “Sentiment Evolution Analysis and Association Rule Mining for COVID-19 Tweets,” J. Digit. Art Humanit., pp. 3–21, 2021.

F. Gao, G. Huang, S. Li, Z. Huang, and L. Chai, “Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data,” ISPRS Int. J. Geo-Information, vol. 10, no. 12, p. 834, 2021.

D. Y. Liliana, A. Hardianto, and M. Ridok, “Indonesian news classification using support vector machine,” World Acad. Sci. Eng. Technol., vol. 81, no. 9, pp. 767–770, 2011, doi: 10.5281/zenodo.1074438.

K. V. Veena and D. Mathew, “Speaker identification and verification of noisy speech using multitaper MFCC and Gaussian Mixture models,” Proc. 2015 IEEE Int. Conf. Power, Instrumentation, Control Comput. PICC 2015, no. Lc, 2016, doi: 10.1109/PICC.2015.7455806.

B. Alexandru-costin, “Comparison of Deep Learning Models for Automatic Detection of Sarcasm Context on the MUStARD Dataset,” 2023.

S. Pradha, M. N. Halgamuge, and N. Tran Quoc Vinh, “Effective text data preprocessing technique for sentiment analysis in social media data,” Proc. 2019 11th Int. Conf. Knowl. Syst. Eng. KSE 2019, pp. 1–8, 2019, doi: 10.1109/KSE.2019.8919368.




DOI: https://doi.org/10.30645/kesatria.v4i4.241

DOI (PDF): https://doi.org/10.30645/kesatria.v4i4.241.g239

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