ANALISIS SENTIMEN PENGGUNA MEDIA SOSIAL TWITTER TERHADAP WABAH COVID-19 DENGAN METODE NAIVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE

Authors

  • Harun Sujadi Universitas Majalengka

DOI:

https://doi.org/10.31949/infotech.v8i1.1883

Keywords:

sentiment analysis, covid-19, naïve bayes classifier, support vector machine

Abstract

Twitter is often used to express opinions about a topic or issue that is trending. In the early 2020 period in Indonesia, Twitter was enlivened by the issue of the COVID-19 virus caused by SARS-CoV-2. Many Twitter users have expressed their views on the COVID-19 issue, which has attracted the attention of several parties to be used as a reference in making new decisions or policies. Therefore, it is necessary to do a sentiment analysis to determine the polarity of the sentiments that are in the contents of the tweets. This study uses the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) methods. with a total dataset of 1652 tweets. From the results of classification using the NBC method, the classification accuracy value is 78.3%. While the accuracy value obtained by the SVM method is 81.6%. While the results of the accuracy test using the Cross Validation method with 10 K-Fold CV results in an average accuracy value of the NBC method of 69.8% and an average accuracy value of the SVM method of 74.4%. It can be concluded that the SVM method is proven to have a higher accuracy value than the NBC method.

Downloads

Download data is not yet available.

References

BNPB. (2020, 6). Data Sebaran. Retrieved from covid19.go.id: https://covid19.go.id/
Buana, A. R. (2020). Problematika Regulasi Ojek Online Dalam Masa Pembatasan Sosial Berskala Besar Covid-19 . Adalah: Buletin Hukum dan Keadilan.
Clinten, B. (2019, 10). Tekno : Pengguna Aktif Harian Twitter Indonesia. Retrieved from tekno.kompas.com: https://tekno.kompas.com/read/2019/10/30/16062477/pengguna-aktif-harian-twitter-indonesia-diklaim-terbanyak
Hadiwardoyo, W. (2020). Kerugian Ekonomi Nasional Akibat Pandemi CoviD-19 . Baskara : Journal of Business and Entrepreneurship .
Kemkes. (2020, April Selasa). Menkes Tetapkan PSBB untuk DKI Jakarta. Retrieved from www.kemkes.go.id: https://www.kemkes.go.id/article/view/20040700003/-menkes-tetapkan-psbb-untuk-dki-jakarta.html
Lee, E. (2018). Cyber physical systems: Design challenges. In Object Oriented Real-Time Distributed Computing (ISORC. 11th IEEE International Symposium, 363-369.
Nooraeni, R., & dkk. (2019). Analisis Sentimen Publik Terhadap Sistem Zonasi Sekolah Menggunakan Data Twitter Dengan Metode Naïve Bayes Classification. Faktor Exacta.
Nurjannah, M., Hamdani, & Astuti, I. F. (2013). Penerapan Algoritma Term Frequency-Inverse Document Frequency (TF-IDF) Untuk Text Mining. Jurnal Informatika Mulawarman, 110.
Prasetyo, E. (2012). Data mining konsep dan aplikasi menggunakan matlab. Yogyakarta: Andi.
Jakarta: Pusat Data dan Informasi.
Rocha, A. D. (2006). Naive Bayes Classifier Teaching Material. Retrieved from www.ic.unicamp.br: http://www.ic.unicamp.br/~rocha/teaching/2011s2/mc906/aulas/naive-bayes-classifier.pdf.
Rodiyansyah, S. F., & Winarko, E. (2013). Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification. IJCCS.
Santosa, B. (2007). Tutorial Support Vector Machine. Surabaya: ITS.
Setiawan, A. R. (2020). Lembar Kegiatan Literasi Saintifik untuk Pembelajaran Jarak Jauh Topik Penyakit Coronavirus 2019 (COVID-19) . EDUKATIF: JURNAL ILMU PENDIDIKAN , 2.
Simorangkir, H., & Lhaksmana, K. M. (2018). Analisis Sentimen pada Twitter untuk Games Online Mobile Legends dan Arena of Valor dengan Metode Naïve Bayes Classifier. e-Proceeding of Engineering.
Syadid, F. (2019). Analisis Sentimen Komentar Nitizen Terhadap Calon Presiden Indonesia 2019 dari Twitter Menggunakan Algoritma Term Frequency-Invers Document Frequency (TFIDF) dan Metode Multi Layer Perceptron (MLP) Neural Network. Respositori UIN Syarif Hidayatullah Jakarta.
WHO. (2020). Pertanyaan dan jawaban terkait Coronavirus. Retrieved from who.int: https://www.who.int/indonesia/news/novel-coronavirus/qa-for-public
Widjaya, A., Hiryanto, L., & Handhayani, T. (2017). Prediksi Masa Studi Mahasiswa Dengan Voting Feature Interval 5 Pada Aplikasi Konsultasi Akademik Online. Journal of Computer Science and Information Systems.
.

Downloads

Published

24-03-2022

How to Cite

Sujadi, H. (2022). ANALISIS SENTIMEN PENGGUNA MEDIA SOSIAL TWITTER TERHADAP WABAH COVID-19 DENGAN METODE NAIVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE . INFOTECH Journal, 8(1), 22–27. https://doi.org/10.31949/infotech.v8i1.1883

Issue

Section

Articles