Analysis of Public Sentiment Towards Goverment Efforts to Break the Chain of Covid-19 Transmission in Indonesia Using CNN and Bidirectional LSTM

  • Gusti Agung Mayun Kukuh Jaluwana Universitas Udayana
  • Gusti Made Arya Sasmita Universitas Udayana
  • I Made Agus Dwi Suarjaya Universitas Udayana
Keywords: COVID-19, Deep Learning, Bidirectional LSTM, CNN

Abstract

COVID-19 is a new disease that has a negatively impacts in Indonesia, so the government is taking several measures to suppress the spread of COVID-19, such as new normal, social distancing, health protocols fines, and COVID-19 vaccination. The government's handling efforts have reaped a variety of negative to positive responses from the public on social media, so this study aims to determine the effectiveness of the government's efforts by analyzing public sentiment using the Deep Learning method with 1,875 training datasets consisting of four types government efforts and taken from various media social. The use of Deep Learning begins with testing several Deep Learning architectures to determine the best architecture for predicting data. The architectures tested include CNN and Bi-LSTM, where from these tests, Bi-LSTM outperforms CNN with the best performance achieving the accuracy of 97.34% and 97.33% for precision, recall, and F1-score. The results of public sentiment analysis show that social distancing efforts are considered the most effective by obtaining the most positive sentiments by 33.93%, while the effort to health protocol fines is considered lacking because it obtains the most negative sentiment of 35.64%, so the government must continue to enforce social distancing and optimize other efforts that are still considered ineffective.

 

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Published
2022-08-22
How to Cite
Kukuh Jaluwana, G. A. M., Gusti Made Arya Sasmita, & I Made Agus Dwi Suarjaya. (2022). Analysis of Public Sentiment Towards Goverment Efforts to Break the Chain of Covid-19 Transmission in Indonesia Using CNN and Bidirectional LSTM. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(4), 511 - 520. https://doi.org/10.29207/resti.v6i4.4055
Section
Artikel Teknologi Informasi