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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Abdul Azis Adjie Sumanjaya
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Analisis Sentimen Data Tweets terhadap Penanganan Covid-19 di Indonesia menggunakan Metode Naive Bayes dan Pemilihan Kata Bersentimen menggunakan Lexicon Based Abdul Azis Adjie Sumanjaya; Indriati Indriati; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 4 (2022): April 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a very popular social media platform in the current millennial era. Twitter is widely used as a means to express opinions and criticisms on issues that are currently being discussed. At the beginning of July the government had made efforts to handle COVID-19 in Indonesia by establishing a policy Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) Darurat. Such a policy is very necessary considering the spread of the COVID-19 virus is still high, especially in big cities. But on the other hand, the limitation of activities as part of the policy has a very large impact on the community, especially with the addition of the extension of the policy which makes people bored because they find it difficult to carry out activities. For this reason, this research conducted a sentiment analysis to see the tendency of public sentiment during the implementation Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) Darurat policy in Indonesia using the Naive Bayes classification method and the addition of the Lexicon Based feature. The provision of the Lexicon Based feature aims to filter sentimental words, so that data processing becomes faster. Based on the test results obtained, through the division of cross validation with the confusion matrix test, the accuracy is 0.75, precision is 0.76, recall is 0.76, and f-measure is 0.75. The use of the stopword feature has an influence on the classification results, because the use of the stopword feature can eliminate some of the terms resulting from the Lexicon Based feature which causes a reduction in term variations so that the accuracy results obtained are lower than without using the stopword feature.
Co-Authors Achmad Ridok Indriati Indriati