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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Aldi Bagus Sasmita
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Analisis Sentimen Komentar pada Media Sosial Twitter tentang PPKM Covid-19 di Indonesia dengan Metode Naive Bayes Aldi Bagus Sasmita; Bayu Rahayudi; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 3 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The ongoing spread of COVID-19 brings many changes, including Indonesia country. The proper handling for each sector to deal with this pandemic is still ongoing with various efforts including the establishment of policies to intercept the expansion of the virus. The government's policy-setting effort to cut the spread of COVID-19 is the Pemberlakuan Pembatasan Kegiatan Masyarakat, known as PPKM. This policy received various responses from the public through many media, especially digital media through personal social media accounts, especially in the form of comments. Twitter social media platform becomes an effective argumentation space, especially for the phenomenon that is being discussed a lot, including the PPKM policy. Various responses in the form of comments need to be analyzed by sentiment with a classification of positive or negative responses that acts as a sentence filter. The reason of this reasearch on the Naive Bayes method is to determine the value of accuracy in the classification of public sentiment on Twitter social media in response to the PPKM policy carried out by the government in Indonesia. The consequence of the research conducted this time stated that the Naive Bayes Classifier algorithm using the NLTK Filtering Library has the highest accuracy such as Tala Filtering Library and the Combined Stopword Filtering Library. The accuracy results obtained by the NLTK Library Filtering is 77.2%, Tala Filtering Library is 76,6%, and The Combined Stopword Filtering Libray is 75,2%.
Co-Authors Bayu Rahayudi Lailil Muflikhah