ANALISIS PENGENALAN POLA PADA KEMAJUAN TIMNAS INDONESIA UNTUK MENGANALISIS REAKSI PUBLIK TENTANG KEMAJUAN TIMNAS INDONESIA
Keywords:
Sentiment analysis, Logistic Regression, Random Forest, SVM, Indonesian National Team, machine learningAbstract
This study aims to analyze public reactions to the progress of the Indonesian National Team using a machine learning-based pattern recognition method. Data is taken from two main sources, namely Twitter and News API, which are then processed through several stages. These stages include text cleaning to remove special characters, URLs, and irrelevant symbols, as well as removing stopwords to improve data quality. The data is then grouped into three main sentiment classes: positive, negative, and neutral. This study implements three machine learning models, namely Logistic Regression, Random Forest, and Support Vector Machine (SVM), to classify text data. The SVM model showed the best performance with the highest accuracy of 90%, followed by Random Forest with an accuracy of 88%, and Logistic Regression with an accuracy of 85%. Additional analysis was conducted to evaluate frequently used words in each sentiment class, which provided additional insights into public opinion. The results of this study provide an important contribution to understanding public reactions to the progress of the Indonesian National Team and demonstrate the effectiveness of machine learning models in text-based sentiment analysis. These findings can be used as a basis for developing more effective communication strategies for stakeholders in supporting the progress of the Indonesian National Team.
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Copyright (c) 2025 Aditya Liandri, Mhd Arief Hasan, Ricky Carlo, Ricko Oktanta, Ginting Ginting, Yosua Alexandro, Niko Tinambunan (Author)
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