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All Journal Enrichment: Journal of Multidisciplinary Research and Development
Yulison H. Chrisnanto
Universitas Jenderal Achmad Yani, Indonesia
Humanities Economics, Econometrics & Finance Engineering Health Professions Social Sciences

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Covid-19 Sentiment Analysis Using Random Forest Classification Salsa Safira Nur Syamsi; Asep Id Hadiana; Yulison H. Chrisnanto
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 6 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i6.53

Abstract

The spread of the COVID-19 pandemic has reached a significant global scale, changing the dynamics of people's lives around the world. Social media platforms such as Twitter have become important channels for individuals to share experiences, voice opinions, and participate in discussions related to this pandemic. Sentiment analysis emerged as an important approach to reveal changes in people's attitudes and emotions in facing this challenge. This research involves analyzing sentiment during the COVID-19 pandemic to understand the feelings, attitudes, and views of the community after the peak phase of the pandemic. This study refers to previous findings which show that the Random Forest Algorithm provides the highest accuracy in this analysis. Through testing with the Random Forest Algorithm method, model accuracy testing is carried out using a confusion matrix and comparing test data and training data in a ratio of 80:20. Test results show that this model achieves an accuracy rate of 91%, providing a more comprehensive view of changes in public sentiment during the COVID-19 pandemic.
Co-Authors Hadiana, Asep Id Salsa Safira Nur Syamsi