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%.
