Klasifikasi Sentimen Masyarakat Terhadap Pemberlakuan Pembatasan Kegiatan Masyarakat Menggunakan Text Mining Pada Twitter
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Asdar Mustofa
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Rice Novita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Corona Virus Disease 2019 (Covid-19) is currently a pandemic in the world, including in Indonesia. Various policies have been carried out to break the chain of the spread of Covid-19, one of which is the government's policy of implementing Community Activity Restrictions (PPKM). PPKM is one of the most discussed topics on social media, including Twitter. Tweets on Twitter given by the public to the PPKM policy that was held to evaluate the implementation of PPKM, it is necessary to classify public sentiment using text mining, in this study using the K-Nearest Neighbor (KNN) and Naïve Bayes Classifier (NBC) algorithms with data from tweets. Twitter during the PPKM last year with 3,516 data. Where the results are that the NBC algorithm is better than the KNN algorithm with an accuracy of 79.67% compared to 78.86%, the polarity of public sentiment towards PPKM is also obtained with positive sentiment of 36.83% with a total of 1,295, neutral sentiment of tweets 54.15% with the number of 1,902 tweets, and 9.02% negative sentiment with a total of 317 tweets
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