<![CDATA[Electric vehicles (EVs) are widely recognized as an environmentally sustainable alternative capable of reducinggreenhouse gas emissions; however, their adoption in Indonesia remains limited. Data from the IndonesianMinistry of Transportation, as recorded in the Type Approval Registration System (SRUT), indicate thatapproximately 195,084 Battery Electric Vehicles (BEVs) were registered nationwide by early 2024. This studyinvestigates public sentiment toward electric vehicles using social media data from X, Instagram, and TikTok,while also comparing the effectiveness of two text classification approaches: Bidirectional EncoderRepresentations from Transformers (BERT) and Long Short-Term Memory (LSTM). A total of 5,172Indonesian-language comments were collected through crawling and scraping techniques using electricvehicle-related keywords over the period January 2021 to January 2025. The comments were categorized intofive sentiment classes: very positive, positive, neutral, negative, and very negative. The analytical processfollowed the Knowledge Discovery in Databases (KDD) framework, including data preprocessing,transformation, classification, and evaluation using a confusion matrix. The results indicate that IndoBERTsubstantially outperformed LSTM, achieving an accuracy of 91% compared to 36% for LSTM. Sentimentanalysis reveals a dominance of negative and very negative opinions, primarily reflecting public concernsregarding cost, performance, and maintenance of electric vehicles. These findings offer important insights forpolicymakers and the automotive industry in designing targeted promotion strategies, improving publicawareness, and strengthening supporting infrastructure. Future research is encouraged to explore dataaugmentation techniques to improve model performance, particularly for deep learning models such as LSTM,in order to better support evidence-based electric vehicle adoption policies.]]>
