<![CDATA[In the rapidly evolving digital era, online gaming platforms like Roblox have transformed into complex interactive social spaces where users interact, create, and co-build virtual experiences. This study aims to compare the performance of two deep learning architectures Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in classifying user reviews of the Roblox app from the Google Play Store into positive, negative, and neutral sentiment categories. The dataset comprises 5,000 Indonesian-language reviews collected via web scraping using the google_play_scraper library. Preprocessing involved text cleaning, case folding, tokenization, normalization of informal words, stopword removal, stemming, and lexicon-based sentiment labeling. Data were converted to numerical representations using Tokenizer and padding, then split into 80% training and 20% testing subsets. CNN achieved superior performance with 89% accuracy, 0.88 precision, 0.81 recall, and 0.83 F1-score, outperforming LSTM (86.60% accuracy, 0.80 precision, 0.82 recall, 0.81 F1-score). CNN effectively extracts spatial patterns in text, while LSTM captures temporal word dependencies. This research affirms CNN's superiority for short Indonesian text sentiment analysis, provides a deep learning benchmark for gaming app reviews, and offers practical implications for Roblox developers to interpret user feedback for feature enhancements.]]>
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