Mapping Facial Expressions Based on Text for Virtual Counseling Chatbot Using IndoBERT Model - Padilah, Rifki; Wijaya, Rifki; Shaufiah, Shaufiah (Jurnal Teknik Informatika (Jutif), 2025)
Early marriage in Lombok remains a serious issue, with a prevalence rate of 16.59% in 2021, the second highest in Indonesia. Limited access to counseling services, especially in rural areas, poses a significant prevention challenge. This study developed a virtual counseling chatbot system capable of mapping text-based emotions to facial expressions to improve the effectiveness of counseling for early marriage prevention. The methodology involved training an IndoBERT model on a synthetic dataset to analyze conversation texts. The model was designed to classify user input into five functional emotion categories: Enthusiasm, Gentleness, Analytical, Inspirational, and Cautionary. Performance evaluation revealed that the IndoBERT model achieved an outstanding accuracy of 94% in its final phase. This result significantly surpassed other models evaluated, such as CNN (71.6%) and KNN (79%), confirming the superiority of the chosen approach The study concludes that the high-accuracy IndoBERT model is a robust foundation for empathetic virtual agents. This research provides a significant contribution to the fields of Affective Computing and Human-Computer Interaction by demonstrating an effective framework for mapping nuanced, functional emotions from Indonesian text to facial expressions. The proposed system not only offers a scalable technological solution for mental health challenges like early marriage prevention but also highlights the impact of advanced, context-aware NLP models in creating more human-like and empathetic user interactions.