Natural Language Understanding for School Bullying Detection and Consultation: A DIET Classifier Approach in RASA Framework - Irawan, Yoan Freddy; Hadiono, Kristophorus (Jurnal Teknik Informatika (Jutif), 2026)
This research presents the development and implementation of a DIET classifier-based chatbot system using the RASA Framework to handle bullying reports at SMP Negeri 3 Ungaran. The system aims to provide 24/7 automated counseling support service, addressing the limitations of traditional human-to-human support systems that often result in delayed responses and reduced user satisfaction. The model was trained using a structured dataset comprising 61 dialogue examples collected through interviews with experienced guidance and counseling teachers, capturing authentic student communication patterns related to bullying issues. The evaluation results demonstrate exceptional performance, achieving 100% accuracy across 12 intent categories, with perfect precision and recall scores. The system successfully distinguishes between various emotional states and counseling needs, providing appropriate responses with high confidence levels. The intent categories include emotional expressions (merasa_dibully, merasa_sedih, merasa_takut), support-seeking behaviors (butuh_nasihat, ingin_bicara_dengan_guru), and conversational elements, ensuring comprehensive coverage of bullying-related communication scenarios. This implementation proves that AI-driven solutions can effectively support educational institutions in providing immediate, accessible counseling assistance while maintaining accuracy in emotional support and bullying prevention. This research contributes to the field of computer science by demonstrating the practical application of natural language understanding frameworks in sensitive educational contexts, advancing AI-driven counseling systems that can be scaled across educational institutions. The study provides a replicable methodology for developing culturally-sensitive AI applications in educational environments, particularly valuable for institutions in developing countries with limited digital mental health resources.