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All Journal IJoICT (International Journal on Information and Communication Technology)
Muhammad Kiko Aulia Reiki
Telkom University
Computer Science & IT

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Comparison of Term Weighting Methods in Sentiment Analysis of the New State Capital of Indonesia with the SVM Method Muhammad Kiko Aulia Reiki; Yuliant Sibaroni; Erwin Budi Setiawan
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 2 (2022): December 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i2.681

Abstract

The relocation of the State Capital to “Nusantara”, which was inaugurated with the enactment of UU No. 3 of 2022, is a significant project that has sparked polemics among Indonesian citizens. Many people expressed their opinions and thoughts regarding the relocation of the State Capital on Twitter. This tendency of public opinion needs to be identified with sentiment analysis. In sentiment analysis, term weighting is an essential component to obtain optimal accuracy. Various people are trying to modify the existing term weighting to increase the performance and accuracy of the model. One of them is icf-based or tf-bin.icf, which combines inverse category frequency (ICF) and relevance frequency (RF). This study compares the tf-idf, tf-rf, and tf-bin.icf term weighting with the SVM classification method on the new State Capital of Indonesia topic. The tf-idf weighting results are still the best compared to the tf-bin.icf and tf-rf term weights, with an accuracy score of 88.0% a 1,3% difference with tf-bin.icf term weighting.
Co-Authors Erwin Budi Setiawan Yuliant Sibaroni