Open AccessCovid-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 -
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%.