Perbandingan Algoritma NBC, SVM dan Random Forest untuk Analisis Sentimen Implementasi Starlink pada Media Sosial X
- Lintang Kencono Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
-
Dedi Darwis
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
(*) Corresponding Author
Abstract
Internet development in Indonesia continues to progress rapidly, but equitable access remains a challenge, especially in remote areas. Starlink, a satellite internet service from SpaceX, comes as a solution to reduce this gap by providing fast and stable connectivity. This research analyzes public sentiment towards the implementation of Starlink on social media platform X through a comparative approach using three Machine Learning algorithms: Naive Bayes Classifier, Support Vector Machine, and Random Forest. The research data consisted of 6,780 Indonesian tweets collected during the period September 1 to November 30, 2024 using the harvest tweet library with the keywords “starlink,” “internet starlink,” and “SpaceX starlink”. After preprocessing, a total of 5,382 tweets were used, consisting of 4,348 tweets with negative sentiment and 884 tweets with positive sentiment. To overcome data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was applied. Before the application of SMOTE, the Random Forest model showed the highest accuracy of 92%, followed by Support Vector Machine with 91%, and Naive Bayes Classifier with 85%. After SMOTE was applied, the accuracy of the three models increased significantly, with Random Forest reaching 99%, Support Vector Machine 98%, and Naive Bayes Classifier 91%. Random Forest also showed the best performance in detecting positive sentiment, with Precision and Recall values reaching 100%. This research provides an in-depth insight into the effectiveness of Machine Learning algorithms in analyzing public sentiment towards Starlink services on social media and shows that the application of SMOTE can improve the model's performance in classifying sentiment more evenly.
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References
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