<![CDATA[Most of the hate speech and abusive content on social media, particularly in the Indonesian language, presents significant challenges for content moderation systems. Previous research has applied machine learning models such as Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) to address this issue. However, these approaches are limited in their ability to capture the relational and contextual nuances inherent in the data, resulting in suboptimal performance. This study introduces an approach by combining Graph Neural Networks (GNN) with Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction to improve hate speech detection on Twitter (platform X). The dataset consists of 13,169 Indonesian tweets, manually labeled for hate speech and abusive categories. Preprocessing steps include text cleaning, stemming, stop-word removal, and normalization. The GNN model achieved superior results, with accuracy scores of 92.90% for Abusive and 89.78% for Hate Speech, significantly outperforming the RNN model, which achieved accuracy of 86.09% and 86.15%, respectively. This study highlights the advantage of graph-based approaches in capturing complex relationships within text data. Future research can explore expanding datasets to include regional dialects and integrating advanced feature extraction techniques like Word2Vec or BERT. This study establishes a robust framework for improving hate speech detection, offering a valuable contribution to safer digital environments.]]>
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