Sentiment Analysis of Southeast Asian Games (SEA Games) in Philippines 2019 Based on Opinion of Internet User of Social Media Twitter with K-Nearest Neighbor and Support Vector Machine

Authors

  • Muhammad Riefky PGRI Adi Buana University
  • Wara Pramesti PGRI Adi Buana University

DOI:

https://doi.org/10.20956/jmsk.v17i1.9947

Keywords:

K-nearest Neighbor, SEA Games, Sentimen, Support Vector Machine

Abstract

Sports events are an activity that is in great demand, especially the people of Southeast Asia. One of the most prestigious sporting events in the Southeast Asian region is the Southeast Asian Games (SEA Games). SEA Games is one of the sporting events held in the Southeast Asia region and is only held every two years involving eleven member countries of the Association of South East Asian Nations (ASEAN). The most SEA Games issues occurred on Twitter with 20,600 tweets. This is because the 2019 SEA Games event in the Philippines experienced many irregularities, one of which is the Rizal Memorium stadium, which has not been renovated until now. The purpose of this study is to obtain and compare the results of the accuracy of the classification of Twitter users' sentiments towards the 2019 SEA Games in the Philippines using k-nearest neighbor and support vector machine. The data used in this study comes from data from Twitter social media users who often use the hashtag "SEA Games 2019" which has been done with text preprocessing of 2697 tweets with data partitions of 60% for training data and 40% for testing data. The conclusion that can be drawn from this research is that the best accuracy results in the k-nearest neighbor and support vector machine classification are the support vector machine classification with a polynomial kernel of 92.96% so that the predictions of the Support Vector Machine classification tend to be negative. 

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Published

2020-08-24

How to Cite

Riefky, M., & Pramesti, W. (2020). Sentiment Analysis of Southeast Asian Games (SEA Games) in Philippines 2019 Based on Opinion of Internet User of Social Media Twitter with K-Nearest Neighbor and Support Vector Machine. Jurnal Matematika, Statistika Dan Komputasi, 17(1), 26-41. https://doi.org/10.20956/jmsk.v17i1.9947

Issue

Section

Research Articles