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Covid-19 Hoax Detection Using KNN in Jaccard Space
https://doi.org/10.22146/ijccs.67392 Ema Utami(1), Ahmad Fikri Iskandar(2*), Wahyu Hidayat(3), Agung Budi Prasetyo(4), Anggit Dwi Hartanto(5)
(1) Magister Teknik Informatika, Univeristas Amikom Yogyakarta, Yogyakarta
(2) Magister Teknik Informatika, Univeristas Amikom Yogyakarta, Yogyakarta
(3) Magister Teknik Informatika, Univeristas Amikom Yogyakarta, Yogyakarta
(4) Magister Teknik Informatika, Univeristas Amikom Yogyakarta, Yogyakarta
(5) Magister Teknik Informatika, Univeristas Amikom Yogyakarta, Yogyakarta
(*) Corresponding Author
Abstract
Social media has become a communication key to spark thinking, dialogue and action around social issues. Hoax is information that added or subtracted from the content of the actual news. The spread of unconfirmed Covid-19 news can cause public concern. The purpose of this research was to modify KNN with Jaccard Space in the classification of hoax news related to Covid-19. The data used from Jabar Saber Hoaks and Jala Hoaks. The classification results with KNN with Jaccard Space and stemming Nazief & Adriani get the highest accuracy than other models in this research. The accuracy of the KNN model on the Jaccard Space with stemming Nazief & Adriani and K = 5 was 75.89%, while for Naïve Bayes was 65.18%.
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DOI: https://doi.org/10.22146/ijccs.67392
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