SENTIMENT ANALYSIS OF THREE-PERIOD POLEMICS USING K-NEAREST NEIGHBOR WITH TF-IDF WEIGHTING

Keywords: Sentiment Analysis, 3 Periods, K-Nearest Neighbor, TF-IDF

Abstract

The issue of changing the presidential term which was originally 2 periods of government into 3 periods raises pros and cons in the community. Many 3-period hashtags have sprung up on social media twitter. So that conducted research on sentiment analysis of presidential election polemics 3 period. The purpose of the study was to produce the value of classification on the issue of presidential election change discourse into 3 periods using the K-NN method and whether the k-NN method proved to be well used for classifying text in the review of presidential election polemics 3 periods. Dataset totaling 1152 data, data is processed using Python and Jupyter Notebook as a text editor. The data is classified into positive reviews and negative reviews, then the data is divided into training data and test data with a ratio of 90:10. Weighting words using TF-IDF and sentiment classification using K-NN method. From the results of classification using the K-NN method obtained the highest accuracy when the value of k=17 and k = 18 with an accuracy of 85.3%. The results of the analysis of public sentiment to review the issue of discourse on the change of presidential term into 3 periods tend to be negative with a percentage of 21.26% positive sentiment and 78.74% negative sentiment.

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Published
2022-06-16
How to Cite
Ernawati, S., & Wati, R. (2022). SENTIMENT ANALYSIS OF THREE-PERIOD POLEMICS USING K-NEAREST NEIGHBOR WITH TF-IDF WEIGHTING. Jurnal Riset Informatika, 4(3), 215-220. https://doi.org/10.34288/jri.v4i3.377
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