<![CDATA[Reviews posted by users on the Google Play Store provide valuable feedback that can be utilized to measure user satisfaction and assess the quality of mobile applications. However, the growing volume of reviews makes manual evaluation increasingly impractical, highlighting the need for automated sentiment analysis techniques. This research proposes the use of the Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm to classify the sentiment of Indonesian-language reviews for the Duolingo application. The review dataset was obtained through web scraping from the Google Play Store and underwent several preprocessing steps, including case folding, text cleaning, word normalization, tokenization, stopword removal, and stemming. After preprocessing, the data were divided into 80% training data and 20% testing data for model development and performance evaluation. The effectiveness of the model was measured using accuracy, precision, recall, and F1-score. The experimental results yielded an accuracy of 94.05%, precision of 88.46%, recall of 92.15%, and an F1-score of 91.17%. These findings indicate that the Bi-LSTM model is capable of capturing sentiment patterns with a high level of reliability, although its ability to classify negative reviews is still influenced by the imbalance between sentiment classes. Overall, the study confirms that Bi-LSTM is a suitable deep learning approach for sentiment classification of application reviews and offers meaningful insights that can support Duolingo developers in evaluating user opinions and enhancing application quality.]]>
