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

Abstract

Every time a new variant of Coronavirus (Covid-19) appears, the
media or news platforms review it to find out whether the new variant
is more dangerous or contagious than before. One of the media or
platforms that is fast in presenting news in videos is YouTube.
YouTube is a social media that can upload videos, watch videos, and
comment on the video. The comment field on YouTube videos cannot
be separated from spam comments that annoy other users who want to
follow or participate in the comment column. Indication of spam
comments is still done by observing one by one; this is very inefficient
and time-consuming. This study aims to create a model that can
classify 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%.