Sentiment Analysis about Large-Scale Social Restrictions in Social Media Twitter Using Algoritm K-Nearest Neighbor

Authors

  • Ikhsan Romli Industrial Engineering, Pelita Bangsa University, Indonesia http://orcid.org/0000-0003-0242-4221
  • Shanti Prameswari R Information Technology, Pelita Bangsa University, Indonesia
  • Antika Zahrotul Kamalia Information Technology, Pelita Bangsa University, Indonesia

DOI:

https://doi.org/10.15575/join.v6i1.670

Keywords:

Sentiment Analysis, K-Nearest Neighbor, Euclidean Distance, Cosine Similarity, Manhattan Distance

Abstract

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.

Author Biography

Ikhsan Romli, Industrial Engineering, Pelita Bangsa University

Teknik Industri

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Published

2021-06-17

Issue

Vol. 6 No. 1 (2021)

Section

Article

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