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

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Analisis Sentimen Tweet Covid-19 Varian Omicron pada Platform Media Sosial Twitter menggunakan Metode LSTM berbasis Multi Fungsi Aktivasi dan GLOVE Alfen Hasiholan; Imam Cholissodin; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 10 (2022): Oktober 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

SARS-CoV-2 virus, also known as COVID-19, has become a very deadly epidemic for the past 2 years. At the end of 2021, the world was threatened by the emergence of a new Covid-19 variant, namely omicron. This variant is referred to as one that is very fast in transmission. The virus was first detected in South Africa and was designated by the world health agency (WHO) as a variant of concern under the name B.1.1.529. This has made omicron a big topic of discussion throughout the world community until now. Social media have played a crucial role in spreading information about the variant of omicron throughout the world. Twitter is a microblogging social media platform that is very effective in sharing lots of information. The number of tweets uploaded every minute is very large, up to 350,000 tweets. This number can be a very useful source of data for obtaining a public opinion on certain topics, especially the covid-19 omicron-related tweets. Sentiment analysis plays an important role in this issue. By using the sentiment analysis method, these opinions can be classified into positive or negative opinions. The long-Short Term Memory algorithm is one of the methods used in classifying the sentiment of public opinion. Optimization of this model is done by using the Glove word embedding method. This method works by counting the occurrence of a word with another word and then converting it into a vector. The result of sentiment analysis using the Long-Short Term Memory and GloVe Embedding method with 100 dimensions resulted in an accuracy rate of 82%.
Co-Authors Imam Cholissodin Novanto Yudistira