Covid-19 Hoax Detection Using KNN in Jaccard Space | Zendy

Ema Utami | Zendy; Ahmad Fikri Iskandar | Zendy; Wahyu Hidayat | Zendy; Agung Prasetyo | Zendy; Anggit Dwi Hartanto | Zendy

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Covid-19 Hoax Detection Using KNN in Jaccard Space

Author(s) -

Ema Utami,

Ahmad Fikri Iskandar,

Wahyu Hidayat,

Agung Prasetyo,

Anggit Dwi Hartanto

Publication year - 2021

Publication title -

ijccs (indonesian journal of computing and cybernetics systems)

Language(s) - English

Resource type - Journals

eISSN - 2460-7258

pISSN - 1978-1520

DOI - 10.22146/ijccs.67392

Subject(s) - jaccard index , hoax , social media , computer science , space (punctuation) , covid-19 , artificial intelligence , pattern recognition (psychology) , world wide web , medicine , alternative medicine , disease , pathology , infectious disease (medical specialty) , operating system

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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