slot maxwin slot gacor slot Thailand slot gacor maxwin rekomendasi slot gacor jpterus66 slot maxwin slot gacor malam ini slot gacor SLOT ONLINE slot bet 200 888slot slot777 slot terbaru slot88 slot gacor malam ini slot maxwin Situs Slot Gacor 888slot slot777 slot terbaru slot88 slot gacor malam ini slot maxwin Situs Slot Gacor slot gacor malam ini slot maxwin Situs Slot Gacor 888slot slot777 slot terbaru slot88 slot gacor malam ini slot maxwin Situs Slot Gacor slot88 rtp slot gacor slot online slot gacor maxwin slot bet 200 slot gacor slot maxwin SLOT THAILAND Slot Gacor Maxwin slot maxwin slot gacor slot Thailand slot gacor maxwin rekomendasi slot gacor jpterus66 slot maxwin slot gacor malam ini slot gacor SLOT ONLINE slot bet 200 888slot slot777 slot terbaru slot88 slot gacor malam ini slot maxwin Situs Slot Gacor 888slot slot777 slot terbaru slot88 slot gacor malam ini slot maxwin Situs Slot Gacor slot gacor malam ini slot maxwin Situs Slot Gacor 888slot slot777 slot terbaru slot88 slot gacor malam ini slot online slot maxwin link slot gacor
TY - JOUR AU - Padilah, Rifki AU - Wijaya, Rifki AU - Shaufiah, Shaufiah PY - 2025/10/22 Y2 - 2025/11/15 TI - Mapping Facial Expressions Based on Text for Virtual Counseling Chatbot Using IndoBERT Model JF - Jurnal Teknik Informatika (Jutif) JA - J. Tek. Inform. (JUTIF) VL - 6 IS - 5 SE - Articles DO - 10.52436/1.jutif.2025.6.5.5039 UR - https://jutif.if.unsoed.ac.id/index.php/jurnal/article/view/5039 SP - 3750-3768 AB - <p>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.</p> ER -