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All Journal ComEngApp : Computer Engineering and Applications Journal Sisforma: Journal of Information Systems
Wahyu Caesarendra
Universiti Brunei Darussalam
Computer Science & IT Education Engineering Library & Information Science

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Journal : Sisforma: Journal of Information Systems

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Sentiment Analysis of Urban Opinions on COVID-19 Handling in Brunei Darussalam Using Lexicon Weighting and Machine Learning Algorithms Usman Ependi; Wahyu Caesarendra
SISFORMA Vol 11, No 1: May 2024
Publisher : Soegijapranata Catholic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/sisforma.v11i1.11957

Abstract

Crisis management of Covid-19 is closely related to how government provides policy measures and monitors the health conditions of residents and others. Residents will provide feedback (opinions) for any services provided by the government. The main issue in this area is understanding residents' opinions to become a source of information for sentiment in public policy. This study aims to analyze sentiment on crisis management of covid-19 in Brunei Darussalam. Lexicon weight and machine learning classifiers (random forest, k-nearest neighbors, naive Bayes, and decision trees) are used for handling this issue. The data used in this study comes from resident opinions on the BruHealth application, which is part of Brunei Darussalam Government Services. Based on the experimental results, the sentiment of crisis management of Covid-19 is positive. Lexicon weight is used as a basis for data labelling in machine learning classification. Classification results using random forest, k-nearest neighbors, naive Bayes, and decision trees get a significant accuracy of 83,8%, 73,7%, 55%, and 84,2%, respectively.
Co-Authors Mohamad Irfan Usman Ependi