Analisis Sentimen Berita Online Terhadap Transportasi Online di Indonesia dengan Metode Naïve Bayes Classifier, Support Vector Machine dan K-Nearest Neighbor
DOI:
https://doi.org/10.47065/bulletincsr.v5i2.477Keywords:
Online news; Sentiment Analyst; Text Mining; Classification AccuracyAbstract
News about online transportation in Indonesia in 2019 until early 2020 has been published in various Indonesian online media, because there is enough information in the form of text without numerical scale, it is difficult to classify information information efficiently without reading the full text. Sentiment analysis is used to automate the process of assessing opinion whether it is positive or negative. Classifying sentiments on news from online news media with the Text Mining process and using the method of increasing the Classification Accuracy / Ensemble Method of Engineering by combining the classification algorithm naïve bayes method, classifier Supporting vector machines and k-nearest neighbors added with the Particle Swarm Optimization method and Vote method The next will be a comparative analysis. The results of the study above get an SVM exam accuracy value even after using the PSO selection feature with the ensemble. Select is still appropriate at 84.16%, Likewise for NB algorithm which gets 79.08% and KNN which gets approval 87.19%. These words will be used to see words related to sentiments that often appear and have the highest weight and can be used to find out positive news articles and negative news articles. And for this research the model that uses KNN algorithm gets the highest accuracy.
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Copyright (c) 2025 Arina Selawati, Yan Rianto, Rachmawati Darma Astuti, Ainun Zumarniansyah, Deny Novianti
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