SENTIMENT ANALYSIS DUE TO "MUDIK" PROHIBITED OF COVID-19 THROUGH TWITTER

  • Sabar Sautomo (1*) STMIK Nusa Mandiri
  • Noor Hafidz (2) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri
  • Yuni Eka Achyani (3) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri
  • Windu Gata (4) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

  • (*) Corresponding Author
Keywords: Mudik, Twitter, Naïve Bayes Classifier, Support Vector Machine

Abstract

“Mudik” is a habit every year for the people of Indonesia to return to their hometowns before the Eid. The existence of the Corona Virus pandemic (COVID-19) hit all over the world, including Indonesia, resulting in a ban from the government to do Mudik. Social media such as Twitter is often used as an expression of some people in commenting on something like the ban on Mudik. Comments on Twitter that are often known as tweets can be used as material for sentiment analysis. However, it is not easy to do sentiment analysis on Twitter, especially comments in Indonesian, because the text is not structured. This study uses data from Indonesian-language tweets containing the word "Mudik," the algorithm model used in this study, Naïve Bayes Classifier and Support Vector Machine, is compared to get accuracy, precision, recall, and F1-score values. From this research, it was concluded that the Naïve Bayes algorithm and Support Vector Machine performed well enough to predict the sentiment of tweets about Mudik on Twitter social media. Naïve Bayes with an accuracy of 82% and f1-score 0.8, while Support Vector Machine with an accuracy of 87% and f1-score 0.87.

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Author Biographies

Noor Hafidz, Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

Student of Masters in Computer Science

Yuni Eka Achyani, Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

Lecturer of System Information study program

Windu Gata, Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

Lecturer of Computer Science study programs

References

B. B. Soebyakto, “Mudik Lebaran,” J. Ekon. Pembang. J. Econ. Dev., vol. 9, no. 1829–5843, pp. 61–67, 2015.

D. Cucinotta and M. Vanelli, “WHO declares COVID-19 a pandemic,” Acta Biomed., vol. 91, no. 1, pp. 157–160, 2020, doi: 10.23750/abm.v91i1.9397.

“Tok! Pemerintah Larang Mudik Lebaran Mulai 24 April 2020,” https://news.detik.com/.

“Pengguna Aktif Harian Twitter Indonesia Diklaim Terbanyak,” Kompas.Com, 2019.

“Twitter Klaim Pengguna Harian Terbanyak Berasal dari Indonesia,” https://wartakota.tribunnews.com/2019/10/30/twitter-klaim-pengguna-harian-terbanyak-berasal-dari-indonesia, 2019.

B. Pratama et al., “Sentiment Analysis of the Indonesian Police Mobile Brigade Corps Based on Twitter Posts Using the SVM and NB Methods,” J. Phys. Conf. Ser., vol. 1201, no. 1, pp. 0–12, 2019, doi: 10.1088/1742-6596/1201/1/012038.

M. I. Komputer and K. J. Pusat, “Sentimen Analisis Operasi Tangkap Tangan KPK Menurut Masyarakat Menggunakan Algoritma Support Vector Machine , Naive Bayes Berbasis Particle Swarm Optimizition,” vol. 12, no. 3, pp. 230–243, 2019, doi: 10.30998/faktorexacta.v12i3.4992.

G. A. Buntoro, “ANALISIS SENTIMEN HATESPEECH PADA TWITTER DENGAN METODE NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE,” vol. 5, no. September, p. 1939, 2016.

A. Bayhaqy, “Analisa sentimen tentang islamophobia,” no. August 2019, 2020.

S. Prayoginingsih and R. P. Kusumawardani, “Klasifikasi Data Twitter Pelanggan Berdasarkan Kategori myTelkomsel Menggunakan Metode Support Vector Machine (SVM,” J. Sisfo, vol. 06, no. 03, pp. 347–382 Sistem, 2017.

J. Pfeffer, K. Mayer, and F. Morstatter, “Tampering with Twitter’s Sample API,” EPJ Data Sci., vol. 7, no. 1, 2018, doi: 10.1140/epjds/s13688-018-0178-0.

S. Sagar, “Twitter Sentiment Analysis Using Vader,” IJARIIT (Volume 4), vol. 4, no. 1, pp. 485–489, 2018, [Online]. Available: https://dataaspirant.com/2018/03/22/twitter-sentiment-analysis-using-r/.

S. Mujilahwati, “Pre-Processing Text Mining Pada Data Twitter,” Semin. Nas. Teknol. Inf. dan Komun., vol. 2016, no. Sentika, pp. 2089–9815, 2016.

S. Thakare, A. Kamble, V. Thengne, and U. R. Kamble, “Document Segmentation and Language Translation Using Tesseract-OCR,” in 2018 13th International Conference on Industrial and Information Systems, ICIIS 2018 - Proceedings, 2018, doi: 10.1109/ICIINFS.2018.8721372.

Published
2020-06-24
How to Cite
[1]
S. Sautomo, N. Hafidz, Y. Achyani, and W. Gata, “SENTIMENT ANALYSIS DUE TO "MUDIK" PROHIBITED OF COVID-19 THROUGH TWITTER”, jitk, vol. 6, no. 1, pp. 7-12, Jun. 2020.
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