Rafly, Mohammad (2024) Analisis Sentimen Opini Masyarakat Indonesia Pengguna Twitter/x Terhadap Pengungsi Rohingya Migrasi Ke Indonesia Menggunakan Metode Random Forest Dan Svm. Other thesis, Universitas Islam Riau.

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Abstract

The arrival of Rohingya refugees in Indonesia has become a prominent national issue, widely discussed on social media platforms like Twitter. This study aims to analyze the sentiment of Indonesian Twitter/X users towards Rohingya refugees migrating to Indonesia using the SVM and Random Forest algorithms, and subsequently compare the results obtained from both models. By doing so, we hope to gain a more systematic and objective understanding of the sentiments, attitudes, and perspectives of the public. The research is conducted through several methodological stages, including data collection from Twitter using the keyword "Pengungsi Rohingya lang:id". Data preprocessing involves cleaning and processing the data to eliminate noise such as links, punctuation, and irrelevant words. A dataset is constructed encompassing positive, negative, and neutral sentiments based on content analysis of tweets. The SVM and Random Forest algorithms are implemented to classify the sentiments from the constructed dataset. Model evaluation is performed using relevant metrics such as accuracy, precision, recall, and F1-score. The results of sentiment classification are analyzed to gain a deeper understanding of public attitudes and perspectives towards Rohingya refugees migrating to Indonesia. This study aims to contribute to understanding the dynamics of public opinion on Indonesian Twitter/X platforms regarding Rohingya refugees migrating to Indonesia and to enhance the understanding of the effective use of SVM and Random Forest algorithms in sentiment analysis.

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