<![CDATA[Users of Apple's App Store applications are increasingly widespread among smartphone users. However, user responses to these apps vary widely. In addition, continuous developments in adding features and editing capabilities have led to the increasing complexity of using these applications. This research aims to analyze the sentiment of application users on Apple's App Store through reviews on the Google Play Store using the Random Forest method. This method was chosen to efficiently identify and group user responses into positive and negative categories. The dataset used in this study includes 5000 reviews, reflecting the diversity of opinions from actively participating users. The data preprocessing stage involves cleaning, case folding, tokenization, stopword removal, and lemmatization to ensure good data quality before sentiment analysis is carried out. Next, word weighting is carried out using the TF-IDF method to assign weight values to words that influence user sentiment. The research results show that the Random Forest method provides a high level of accuracy in analyzing user sentiment for Apple's App Store applications, with an accuracy of 86%, precision of 89%, recall of 81%, and f1-score of 85%. This research provides further understanding regarding user responses to Apple's App Store applications, and confirms the success of the Random Forest method in handling sentiment analysis on user review datasets on the Google Play Store.]]>
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