<![CDATA[The increasing adoption of electric vehicles (EVs) in Indonesia has generated diverse and unstructured public opinions across social media platforms. This study applies Aspect-Based Sentiment Analysis (ABSA) to classify Indonesian EV opinions across six predefined aspects: battery, design, price, performance, infrastructure, and general perception. The ABSA process was conducted using manual multi-label aspect-sentiment annotation rather than fully automatic aspect extraction, allowing one review to contain multiple EV aspects with corresponding sentiment labels. A total of 3,000 reviews were collected from YouTube, Instagram, and TikTok and used to evaluate three deep learning architectures: Bi-LSTM, IndoBERT, and Hybrid IndoBERT-BiLSTM. In the Hybrid architecture, IndoBERT was used as a Transformer-based contextual feature extractor, and the resulting contextual embeddings were passed into a Bi-LSTM layer to capture bidirectional sequential dependencies before final sentiment classification. The dataset was divided into training, validation, and testing sets using an 80:10:10 split. Model performance was measured using accuracy, precision, recall, and F1-score. The results show that IndoBERT achieved the highest average accuracy of 80.50%, followed by Hybrid IndoBERT-BiLSTM with 78.83% and Bi-LSTM with 71.67%. Although IndoBERT performed best overall, the Hybrid model showed competitive performance and better validation-loss stability in selected aspect-level contexts. These findings indicate the effectiveness of Transformer-based models for Indonesian EV sentiment analysis, while hybrid sequential modeling can provide a stable alternative for handling informal social media text.]]>
