Deteksi Berita Palsu Tentang Vaksinasi Covid-19 Dengan Menggunakan Text Mining Dan Algoritma Cosine Similarity

 (*)Diana Marta Mail (Universitas Budi Darma, Medan, Indonesia)
 Guidio Leonarde Ginting (Universitas Budi Darma, Medan, Indonesia)
 A.M Hatuaon Sihite (Universitas Budi Darma, Medan, Indonesia)

(*) Corresponding Author

Abstract

Indonesia is currently experiencing an outbreak of a global virus called COVID-19. A lot of the fake news currently spreading on social media is news about vaccinations. The truth of spreading fake news should be detected by applying one of the information technologies, namely natural language processing (NLP). The algorithms that will be used to support fake news detection are text mining algorithms and cosine similarity algorithms. Looking for references related to the problem, starting from looking for books that discuss the algorithm used in this research. Looking for data related to the research topic, namely the COVID-19 vaccination. Data processing is done by applying a text mining algorithm as text processing. The stages of text mining are case folding, tokenizing, filtering, stemming. The processing analysis stage is carried out by applying the cosine similarity algorithm to analyze the level of similarity between data. Based on the calculation using the cosine similarity algorithm above, Q (detected news) has a similarity value of above 40% to D31 and D59, where the labels that have been made on D31 and D59 news data are hoax news. The probability of news appearing using a mathematical formula to calculate the probability, the probability value obtained by Q (news detected) is 100% hoax and 0% fact. Based on the results of the analysis of the data by applying the text mining and cosine similarity algorithms, the results show that the identified news is declared a hoax according to the data held.

Keywords


News; Vaccination; Covid-19; TF-IDF; Text Mining; Cosine Similarity

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Copyright (c) 2022 Diana Marta, Guidio Leonarde Ginting, A.M Hatuaon Sihite

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