Application of Bidirectional Encoder Representations from Transformers (BERT) in Sentiment Analysis of COVID-19 Booster Vaccine

Ariansyah, Faizal and Setyaningsih, Sri and Herdianto Situmorang, Boldson (2024) Application of Bidirectional Encoder Representations from Transformers (BERT) in Sentiment Analysis of COVID-19 Booster Vaccine. Skripsi thesis, Universitas Pakuan.

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Abstract

Application of Bidirectional Encoder Representations from Transformers (BERT) in Sentiment Analysis of COVID-19 Booster Vaccine Faizal Ariansyah1 , Sri Setyaningsih2 , Boldson Herdianto Situmorang3 * 1,2,3 Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan University, Bogor, West Java, 16143, Indonesia Abstract Covid-19 was a huge outbreak across the world, including Indonesia, where it affected people in every aspect of life. To minimize the death toll and reduce the infection rates, WHO strongly suggests the government start running Covid-19 vaccines. Not only that but also the injection of booster vaccines is required to help boost people’s immunity in facing Covid-19 viruses that have been regularly mutated. The requirement of having booster vaccines resulted in many pros and cons by the public. To understand the perspective coming from Indonesian, this study carried out sentiment analysis of public response to Covid-19 booster through Twitter or X. Sentiment analysis in this study used the Knowledge Discovery in Database (KDD) algorithm and Bidirectional Encoder Representations from Transformers (BERT) as machine learning for processing classification and modeling tweet data. This research revealed good fit graphic loss results with a research accuracy of 85% based on a confusion matrix from 80% of training data and 20% of test data among 1827 data tweets. Topic modeling is divided into positive topics which include the topics of discipline, facilities, effectiveness and achievement meanwhile negative topics which include the topics of side effects, worry and loss of confidence. All authors contributed equally to this study. Keywords: BERT; booster; sentiment analysis; topic modeling; twitter

Item Type:	Thesis (Skripsi)
Subjects:	Fakultas Ilmu Pengetahuan Alam dan Matematika > Ilmu Komputer
Divisions:	Fakultas Matematika dan Ilmu Pengetahuan Alam > Ilmu Komputer
Depositing User:	PERPUSTAKAAN FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNPAK
Date Deposited:	17 May 2024 03:25
Last Modified:	17 May 2024 03:25
URI:	http://eprints.unpak.ac.id/id/eprint/7678

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