<![CDATA[The rising trend of flexing on TikTok has created a dynamic digital space that often triggers varied user reactions, including subtle forms of cyberbullying. This study aims to analyze public sentiment toward flexing content and evaluate the performance of the Artificial Neural Network (ANN) algorithm in classifying user comments. A total of 4,013 comments were collected through a scraping process on the TikTok account of Miechel Halim and automatically labeled using a lexicon-based approach. The comments were then pre-processed and transformed into Term Frequency–Inverse Document Frequency (TF-IDF) representations before being split into training and testing datasets with an 80:20 ratio. The ANN model was trained under two scenarios before and after the application of the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Experimental results show that the initial model achieved an accuracy of 89.79%, which increased to 90.04% after SMOTE, accompanied by an improvement in the recall of the negative class. These findings indicate that ANN is effective for sentiment classification of TikTok comments, although informal language patterns and highly imbalanced labels remain challenges in identifying negative or potentially harmful remarks related to cyberbullying.]]>
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