Public Sentiment Analysis on Facebook Posts About Shin Tae-Yong's Dismissal Using the K-Nearest Neighbors Algorithm

Syahidan

doi:10.59934/jaiea.v5i1.1611

Authors

Syahidan STMIK KAPUTAMA

DOI:

https://doi.org/10.59934/jaiea.v5i1.1611

Keywords:

Sentiment Analysis, K-Nearest Neighbors, Shin Tae-yong, PSSI, Facebook

Abstract

The sacking of Indonesian national team coach Shin Tae-yong by PSSI on January 6, 2025 sparked massive public attention on various social media platforms, one of which was Facebook. The large volume of unstructured opinions required analysis to accurately understand public perception. This study aims to classify public sentiment toward the news of Shin Tae-yong's dismissal using the K-Nearest Neighbors (K-NN) machine learning method. The data used consists of public comments from Facebook, processed through a series of text preprocessing steps and word weighting using TF-IDF. The K-NN model was tested with a value of k = 80. The results show that the classification model achieved an overall accuracy rate of 76%. While the model performed well for positive and negative sentiment classes, its performance was very weak in identifying neutral sentiment (recall 0.02). The sentiment distribution results indicate that public opinion is dominated by positive sentiment at 56.5%, followed by negative sentiment (29.3%), and neutral sentiment (14.2%). The main finding of this study, which contradicts common assumptions, is that the public response on Facebook to this policy is predominantly positive.

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