<![CDATA[This study aims to analyze the sentiment of user reviews on the Netflix application obtained from the Google Play Store using machine learning and deep learning approaches. The research data were collected through web scraping using the Instan Data Scraper tool and produced 1,661 review data. The dataset was processed through several preprocessing stages, including cleaning, case folding, tokenization, stemming, and stopword removal. Word2Vec was applied to transform text data into numerical vector representations. The classification process was carried out using six algorithms, namely Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Logistic Regression, and Long Short-Term Memory (LSTM). Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results show that the LSTM algorithm achieved the highest accuracy of 88%, outperforming other machine learning models. These findings indicate that deep learning methods are more effective in capturing contextual information in textual data. This research concludes that the LSTM algorithm is the best method for classifying sentiment in Netflix user reviews due to its superior performance in understanding sequential text patterns.]]>
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