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All Journal International Journal of Applied Mathematics and Computing.
Frencis Matheos Sarimolle
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Computer Science & IT Mathematics

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Analisis Sentimen Publik terhadap Hashtag #kaburajadulu Menggunakan Kombinasi Algoritma Support Vector Machine (SVM) dan Random Forest Yuma Akbar; Frencis Matheos Sarimolle; Dwi Swasono Rachmad; Muhammad Derry Oktaviandi
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.129

Abstract

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.
Co-Authors Akbar, Yuma Dwi Swasono Rachmad Muhammad Derry Oktaviandi