TY - GEN
T1 - Analysis the Opinion of School-from-Home during the COVID-19 Pandemic using LSTM Approach
AU - Muqtadiroh, Feby Artwodini
AU - Purwitasari, Diana
AU - Yuniarno, Eko Mulyanto
AU - Nugroho, Supeno Mardi Susiki
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/21
Y1 - 2021/7/21
N2 - The purpose of opinion analysis in this research is to perceive public responses concerning School-From-Home (SFH) policy during the pandemic in attempt to curb virus spread and worry about new cluster emergences. The policy entails diverse reactions from the societies, including the citizens in virtual world through their chirps in social media, such as Twitter. Analysis on the social media has proved that it has remarkable potentials to apprehend public opinions on various issues. The opinion analysis was performed to get insights about public perception towards SFH policy. As initially predicted, the result of our analysis would show that the public perceptions towards SFH would be mainly negative. The researcher adopted LSTM model as a deep learning approach. Moreover, implementing the N-Gram extraction technique was able to improve the model's performance. Model performance accuracy reached 83.30%. It is concluded that the increasing of model accuracy is about 0.018%. While the running time efficiency of LSTM has improved 19.4%. The results of the analysis of SFH's opinion were 77.90% negative and 22.10% positive.
AB - The purpose of opinion analysis in this research is to perceive public responses concerning School-From-Home (SFH) policy during the pandemic in attempt to curb virus spread and worry about new cluster emergences. The policy entails diverse reactions from the societies, including the citizens in virtual world through their chirps in social media, such as Twitter. Analysis on the social media has proved that it has remarkable potentials to apprehend public opinions on various issues. The opinion analysis was performed to get insights about public perception towards SFH policy. As initially predicted, the result of our analysis would show that the public perceptions towards SFH would be mainly negative. The researcher adopted LSTM model as a deep learning approach. Moreover, implementing the N-Gram extraction technique was able to improve the model's performance. Model performance accuracy reached 83.30%. It is concluded that the increasing of model accuracy is about 0.018%. While the running time efficiency of LSTM has improved 19.4%. The results of the analysis of SFH's opinion were 77.90% negative and 22.10% positive.
KW - Covid-19
KW - Deep Learning
KW - LSTM
KW - N-Gram
KW - Opinion Mining
KW - SFH
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85114627935&partnerID=8YFLogxK
U2 - 10.1109/ISITIA52817.2021.9502206
DO - 10.1109/ISITIA52817.2021.9502206
M3 - Conference contribution
AN - SCOPUS:85114627935
T3 - Proceedings - 2021 International Seminar on Intelligent Technology and Its Application: Intelligent Systems for the New Normal Era, ISITIA 2021
SP - 408
EP - 413
BT - Proceedings - 2021 International Seminar on Intelligent Technology and Its Application
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Seminar on Intelligent Technology and Its Application, ISITIA 2021
Y2 - 21 July 2021 through 22 July 2021
ER -