<![CDATA[The rapid advancement of digital technology has driven significant growth in Indonesia’s e-commerce sector, with Shopee emerging as one of the largest platforms generating millions of product reviews daily. These reviews contain valuable consumer opinions that can be analyzed to assess customer satisfaction, yet their massive volume makes manual analysis inefficient and subjective. This study aims to develop an automated sentiment analysis model using DistilBERT to classify Shopee product reviews into positive and negative sentiments. The dataset comprises approximately 1 million Englishlanguage reviews covering various product categories, including electronics, fashion, beauty, and household items. The research methodology involves text preprocessing, tokenization using DistilBertTokenizerFast, and fine-tuning of the DistilBERT model under multiple data-split ratios (90:10, 80:20, 70:30, 60:40). Experimental results demonstrate that DistilBERT achieved the highest accuracy of 94.8%, outperforming baseline models such as Naïve Bayes (88.4%) and SVM (89.6%). These findings confirm that DistilBERT effectively maintains a balance between accuracy, precision, and recall while offering high computational efficiency. This research contributes both methodologically and practically by establishing DistilBERT as a scientifically robust and resource-efficient solution for large-scale sentiment analysis in Indonesia’s e-commerce environment.]]>
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