<![CDATA[Corona Virus 2019 (COVID-19) has now spread rapidly throughout the world since December 2019, so quarantine is carried out to limit the spread of the disease. The implementation of quarantine raises pros and cons from the public which makes the public express all their opinions and criticisms via Twitter. However, every tweet uploaded by the public does not contain the appropriate meaning so a sentiment analysis is necessary. The classification mechanism can be used to determine the polarity of sentiments but classification has its drawbacks. In the classification there is an unsupervised classification or clustering. The K-Means method is often used for clustering, but it still has weaknesses. Therefore, this study conducted a sentiment clustering on Twitter about public opinion of quarantine during COVID-19 pandemic using the DBSCAN method. Based on the results of tests carried out with 200 data, the best silhouette coefficient value is 0.32 at an epsilon value of 20 and a minPts value of 15, while the best davies bouldin index value is 0.10 at an epsilon value of 15 and a minPts value of 15. This research also gets more analysis results on neutral sentiment, which means that the public is of a neutral opinion towards quarantine during the COVID-19 pandemic.]]>
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