Open AccessSentiment Analysis about Large-Scale Social Restrictions in Social Media Twitter Using Algoritm K-Nearest Neighbor
Author(s) -
Ikhsan Romli,
Shanti Prameswari R,
Antika Zahrotul Kamalia
Publication year - 2021
Publication title -
join (jurnal online informatika)
Language(s) - English
Resource type - Journals
eISSN - 2528-1682
pISSN - 2527-9165
DOI - 10.15575/join.v6i1.670
Subject(s) - k nearest neighbors algorithm , euclidean distance , similarity (geometry) , nearest neighbor search , cosine similarity , scale (ratio) , social media , euclidean geometry , computer science , pattern recognition (psychology) , point (geometry) , best bin first , artificial intelligence , mathematics , geography , cartography , world wide web , image (mathematics) , geometry
Sentiment analysis is a data processing to recognize topics that people talk about and their sentiments toward the topics, one of which in this study is about large-scale social restrictions (PSBB). This study aims to classify negative and positive sentiments by applying the K-Nearest Neighbor algorithm to see the accuracy value of 3 types of distance calculation which are cosine similarity, euclidean, and manhattan distance for Indonesian language tweets about large-scale social restrictions (PSBB) from social media twitter. With the results obtained, the K-Nearest Neighbor accuracy by the Cosine Similarity distance 82% at k = 3, K-Nearest Neighbor by the Euclidean Distance with an accuracy of 81% at k = 11 and K-Nearest Neighbor by Manhattan Distance with an accuracy 80% at k = 5, 7, 9, 11, and 13. So, in this study the K-Nearest Neighbor algorithm with the Cosine Similarity Distance calculation gets the highest point.