Authors:

Theresia Hendrawati, Christina Purnama Yanti

Abstract:

“This research tries to take advantage of Twitter by analyzing Indonesian-language tweets that discuss the Covid-19 virus outbreak to find out what Twitter users think about the Covid-19 virus outbreak. This study tries to analyze sentiment to see opinions on Covid-19 tweets that contains Posittive, Negative or Neutral sentiments using Multi-layer Perceptron (MLP) using Backprogragation with Adam optimization. We collected 200 documents (tweets) in Indonesian about Covid-19 that were tweeted since November 2019 and then trained them to get our MLP Backpropagation model. Our model managed to get an accuracy of up to 70% with f1-scores for positive, negative, and neutral classes respectively 0.77, 0.75, and 0.5 from a maximum value of 1. This shows that our model is quite successful in carrying out the sentiment classification process for Indonesian tweets with the Covid-19 theme.”

Keywords

Keyword Not Available

Downloads:

Download data is not yet available.

References

  • [1] M. A. Maulana, A. Setyanto, and M. P. Kurniawan, “Analisis Sentimen Media Sosial Universitas Amikom Yogyakarta Sebagai Sarana Penyebaran Informasi Menggunakan Algoritma Klasifikasi SVM,” SEMNASTEKNOMEDIA ONLINE, vol. 6, no. 1, pp. 1–2, 2018.
  • [2] A. F. Hidayatullah and A. S. Azhari, “Analisis Sentimen dan Klasifikasi Kategori terhadap tokoh publik pada twitter,” in Seminar Nasional Informatika (SEMNASIF), 2015.
  • [3] T. Pramiyati, A. Purwarianti, and I. Supriana, “KECENDERUNGAN PENILAIAN PENGGUNA INFORMASI TERHADAP TWEET (KICAUAN) PADA MEDIA SOSIAL TWITTER,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 7, no. 1, pp. 209–216, 2016.
  • [4] Y. Yuliana, “Corona virus diseases (Covid-19): Sebuah tinjauan literatur,” Wellness Heal. Mag., vol. 2, no. 1, pp. 187–192, 2020.
  • [5] B. Andrianto and S. A. Indriati, “Analisis Sentimen Konten Radikal Melalui Dokumen Twitter Menggunakan Metode Backpropagation,” J. Pengemb. Teknol. Įnformasį dan Įlmu Komput., vol. 2, no. 12, pp. 7380–7385, 2018.
  • [6] F. Syadid, “Analisis sentimen komentar netizen terhadap calon presiden Indonesia 2019 dari twitter menggunakan algoritma term frequency-invers document frequency (tf-idf) dan metode multi layer perceptron (mlp) neural network,” 2019.
  • [7] Y. S. Mahardhika and E. Zuliarso, “ANALISIS SENTIMEN TERHADAP PEMERINTAHAN JOKO WIDODO PADA MEDIA SOSIAL TWITTER MENGGUNAKAN ALGORITMA NAIVES BAYES CLASSIFIER,” SINTAK, vol. 2, no. Nov, 2018.
  • [8] I. W. A. Setyadi, D. C. Khrisne, and I. M. A. Suyadnya, “Automatic Text Summarization Menggunakan Metode Graph dan Ant Colony Optimization,” Maj. Ilm. Teknol. Elektro, vol. 17, no. 1, pp. 124–130, 2018.
  • [9] D. C. Khrisne and I. M. A. Suyadnya, “RUNCING: an Indonesian Text Summarization System Using Cat Swarm Optimization.”

PDF:

https://jurnal.harianregional.com/jeei/full-65626

Published

2021-02-27

How To Cite

HENDRAWATI, Theresia; YANTI, Christina Purnama. Analysis of Twitter Users Sentiment against the Covid-19 Outbreak Using the Backpropagation Method with Adam Optimization.Journal of Electrical, Electronics and Informatics, [S.l.], v. 5, n. 1, p. 1-4, feb. 2021. ISSN 2622-0393. Available at: https://ojs.unud.ac.id/index.php/JEEI/article/view/65626. Date accessed: 28 Aug. 2025. doi:https://doi.org/10.24843/JEEI.2021.v05.i01.p01.

Citation Format

ABNT, APA, BibTeX, CBE, EndNote - EndNote format (Macintosh & Windows), MLA, ProCite - RIS format (Macintosh & Windows), RefWorks, Reference Manager - RIS format (Windows only), Turabian

Issue

Vol 5 No 1 (2021): JEEI (February 2021)

Section

Articles

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License