Comparison of Support Vector Machines and K-Nearest Neighbor Algorithm Analysis of Spam Comments on Youtube Covid Omicron

Sudianto Sudianto (Informatics Engineering, Telkom Institute of Technology Purwokerto)
Juan Arton Arton Masheli (Informatics Engineering, Telkom Institute of Technology Purwokerto)
Nursatio Nugroho (Informatics Engineering, Telkom Institute of Technology Purwokerto)
Rafi Wika Ananda Rumpoko (Informatics Engineering, Telkom Institute of Technology Purwokerto)
Zarkasih Akhmad (Informatics Engineering, Telkom Institute of Technology Purwokerto)

TEKNIK INFORMATIKA
Vol 15, No 2 (2022): JURNAL TEKNIK INFORMATIKA

Every time a new variant of Coronavirus (Covid-19) appears, themedia or news platforms review it to find out whether the new variantis more dangerous or contagious than before. One of the media orplatforms that is fast in presenting news in videos is YouTube.YouTube is a social media that can upload videos, watch videos, andcomment on the video. The comment field on YouTube videos cannotbe separated from spam comments that annoy other users who want tofollow or participate in the comment column. Indication of spamcomments is still done by observing one by one; this is very inefficientand time-consuming. This study aims to create a model that canclassify spam on YouTube comments. The classification method uses the SVM (Support Vector Machines) algorithm and the KNN (K-Nearest Neighbor) algorithm to identify spam comments or not with comment data taken from Omicron's Covid-19 news video on national news channels. The classification results show that the SVM method is superior inaccuracy with the Linear SVC algorithm of 75.12%, SVC of 76.11%, and Nu-SVC of 77.11%. While the KNN algorithm with k=2 is 65.67%, k=4 is 64.51%, k=6 is 62.35%.