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Tubagus Putra, Fauzan Herdika
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Computer Science & IT

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Long Short Term Memory Approach for Sentiment Analysis on COVID-19 Vaccination Policy Tubagus Putra, Fauzan Herdika; Zulfikar, Wildan Budiawan; Lukman, Nur
CoreID Journal Vol. 2 No. 2 (2024): July 2024
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v2i2.33

Abstract

COVID-19 vaccination is one of the efforts to reduce the spread of COVID-19 and reduce the impact or severe symptoms of COVID-19. On social media, many Indonesians have expressed their opinions regarding the COVID-19 vaccine. With technology, we can classify Indonesian public opinion on the COVID-19 vaccine on social media, including pros or cons. Sentiment analysis using the LSTM (Long Short Term Memory) algorithm is one way. The data that has been taken will go through a cleaning and weighting process using Word2Vec before entering the LSTM algorithm. With the evaluation method of the K-Fold Cross Validation model, we can determine the performance of this LSTM algorithm. The results of the performance of this LSTM model show an average accuracy of 74.1% and have the best accuracy in the 4th Fold, which is 81%. The data that has been taken will be tested on this best model, and the results of the sentiment analysis of Indonesian public opinion on the COVID-19 vaccine are 49.4% Positive and 50.6% Negative.
Co-Authors Nur Lukman Wildan Budiawan Zulfikar