Covid-19 Hoax Detection Using KNN in Jaccard Space
| Main Authors: | Utami, Ema, Iskandar, Ahmad Fikri, Hidayat, Wahyu, Prasetyo, Agung Budi, Hartanto, Anggit Dwi |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia
, 2021
|
| Subjects: | |
| Online Access: |
https://journal.ugm.ac.id/ijccs/article/view/67392 https://journal.ugm.ac.id/ijccs/article/view/67392/31839 |
| ctrlnum |
article-67392 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Covid-19 Hoax Detection Using KNN in Jaccard Space</title><creator>Utami, Ema</creator><creator>Iskandar, Ahmad Fikri</creator><creator>Hidayat, Wahyu</creator><creator>Prasetyo, Agung Budi</creator><creator>Hartanto, Anggit Dwi</creator><subject lang="en-US">Computer Science, Artificial Intelligent, Machine Learning</subject><subject lang="en-US">Hoax; Covid-19; KNN; Jaccard; Nazief & Adriani</subject><description lang="en-US">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%.</description><publisher lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</publisher><contributor lang="en-US"/><date>2021-07-31</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Other:</type><type>File:application/pdf</type><identifier>https://journal.ugm.ac.id/ijccs/article/view/67392</identifier><identifier>10.22146/ijccs.67392</identifier><source lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 3 (2021): July; 255-264</source><source>2460-7258</source><source>1978-1520</source><language>eng</language><relation>https://journal.ugm.ac.id/ijccs/article/view/67392/31839</relation><rights lang="en-US">Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</rights><rights lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</rights><recordID>article-67392</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other Other: File:application/pdf File Journal:eJournal |
| author |
Utami, Ema Iskandar, Ahmad Fikri Hidayat, Wahyu Prasetyo, Agung Budi Hartanto, Anggit Dwi |
| title |
Covid-19 Hoax Detection Using KNN in Jaccard Space |
| publisher |
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia |
| publishDate |
2021 |
| topic |
Computer Science Artificial Intelligent Machine Learning Hoax Covid-19 KNN Jaccard Nazief & Adriani |
| url |
https://journal.ugm.ac.id/ijccs/article/view/67392 https://journal.ugm.ac.id/ijccs/article/view/67392/31839 |
| contents |
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%. |
| id |
IOS1094.article-67392 |
| institution |
Universitas Gadjah Mada |
| institution_id |
19 |
| institution_type |
library:university library |
| library |
Perpustakaan Pusat Universitas Gadjah Mada |
| library_id |
488 |
| collection |
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| repository_id |
1094 |
| subject_area |
Program Komputer dan Teknologi Informasi |
| city |
SLEMAN |
| province |
DAERAH ISTIMEWA YOGYAKARTA |
| repoId |
IOS1094 |
| first_indexed |
2022-01-01T17:48:54Z |
| last_indexed |
2022-01-01T17:48:54Z |
| recordtype |
dc |
| _version_ |
1722526201908559872 |
| score |
17.60897 |