<![CDATA[The growth of e-commerce in Indonesia has led to an increasing volume of customer reviews containing vital information. These reviews are generally in the form of unstructured text, necessitating text analysis methods to extract meaningful insights. This study aims to analyze topics and sentiments in customer reviews of the e-commerce platform ruparupa.com by utilizing Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT) algorithms. The LDA algorithm is used to identify the main topics frequently discussed by customers, while BERT is employed to classify review sentiments into positive, negative, and neutral categories. By using Lexicon-Based and VADER as an automatic labeling mechanism (auto-labeling), the preprocessing stage includes cleaning, case folding, and stemming using the Sastrawi library to ensure the quality of the input data. The LDA algorithm is implemented to extract latent topic structures, which are then mapped into five main categories: Price, Application, Service, Product Quality, and Delivery. Furthermore, the DistilBERT model is trained through a fine-tuning process using the AdamW optimizer for 3 epochs. The sentiment analysis results indicate that the model demonstrates very strong performance, as reflected by high accuracy and consistently optimal precision, recall, and F1-score across all sentiment classes. This customer sentiment distribution reflects the level of user satisfaction with the services of ruparupa.com. The combination of LDA and BERT methods is proven effective in providing an overview of key issues and customer perceptions]]>
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