<![CDATA[The rapid growth of social media has increased the use of Indonesian–English code-mixed language in digital communication, particularly on social media X. The non-formal characteristics of social media text, such as slang, abbreviations, emojis, and language switching within a single sentence, make sentiment analysis more challenging than monolingual text. This study aims to perform sentiment classification on code-mixed text by evaluating the performance of the lightweight Transformer model DistilBERT and comparing it with IndoBERTweet. The study adopts the CRISP-DM methodology, which consists of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment stages. The dataset comprises 1,108 primary data collected from social media X between 2022 and 2026 and 5,048 secondary data obtained from previous research. Three experimental scenarios were applied: translation into Indonesian, translation into English, and raw data without translation. The results show that DistilBERT achieved its best performance on the English translation scenario, with accuracies of 73.66% on the primary dataset and 72.00% on the secondary dataset. Meanwhile, IndoBERTweet obtained the highest performance on the Indonesian translation scenario, achieving an accuracy of 79.01%. These findings indicate that the alignment between the language of the input data and the pre-training characteristics of the model significantly affects sentiment classification performance on non-formal code-mixed text.]]>
