<![CDATA[The increasing spread of online gambling promotions in YouTube comment sections poses a serious digital security threat, particularly as perpetrators often disguise words, symbols, and writing patterns to evade automatic moderation systems. This condition highlights the need for an adaptive detection system capable of understanding linguistic variations and contextual patterns. This study aims to design and develop a web-based system for detecting online gambling promotional comments using the Long Short-Term Memory (LSTM) method integrated with the YouTube Data API v3 for real-time comment retrieval. The data processing stages include text cleaning, tokenization, vector transformation using word embeddings, LSTM model training, and performance evaluation. The system is implemented using Laravel as the backend platform, while the deep learning model is developed using Python. The results indicate that the LSTM model is able to classify comments containing online gambling promotions, including those using disguised spelling patterns, based on evaluation using an independent test dataset. The developed system allows users to view detection results and remove flagged comments directly through the dashboard. This research contributes to the development of an integrated and practical web-based content moderation system for digital platforms. ]]>
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