Natural Language Understanding for School Bullying Detection and Consultation: A DIET Classifier Approach in RASA Framework

Yoan Freddy Irawan; Kristophorus Hadiono

doi:10.52436/1.jutif.2026.7.1.4730

Authors

Yoan Freddy Irawan Graduate School of Information Technology, Faculty of Information Technology and Industries, Stikubank University, Semarang, Indonesia
Kristophorus Hadiono Graduate School of Information Technology, Faculty of Information Technology and Industries, Stikubank University, Semarang, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.1.4730

Keywords:

Bullying Prevention, Chatbot, Counseling Support, RASA Framework

Abstract

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.

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References

J. F. Faiz et al., “Analisis Faktor Penyebab Perilaku Penyalahgunaan Narkoba Ditinjau dari Perspektif Islam dan Kesehatan Masyarakat: Literatur review,” J. Relig. Public Heal., vol. 5, no. 1, pp. 26–37, 2023.

G. Mehta, G. Mittra, and V. K. Yadav, “Application of IoT to optimize Data Center operations,” in 2018 International Conference on Computing, Power and Communication Technologies (GUCON), Sep. 2018, pp. 738–742, doi: 10.1109/GUCON.2018.8674939.

L. I. Lafrance St-Martin and S. Villeneuve, “The uses of chatbots in the context of children and teenagers bullying: a systematic literature review,” Cogent Educ., vol. 11, no. 1, pp. 1–11, Dec. 2024, doi: 10.1080/2331186X.2024.2312032.

D. G. S. Ruindungan and A. Jacobus, “Chatbot Development for an Interactive Academic Information Services using the Rasa Open Source Framework,” J. Tek. Elektro dan Komput., vol. 10, no. p-ISSN : 2301-8402, e-ISSN : 2685-368X ,available at: https://ejournal.unsrat.ac.id/index.php/elekdankom, pp. 61–68, 2021.

M. A. Kuhail, N. Alturki, S. Alramlawi, and K. Alhejori, “Interacting with educational chatbots: A systematic review,” Educ. Inf. Technol., vol. 28, no. 1, pp. 973–1018, Jan. 2023, doi: 10.1007/s10639-022-11177-3.

M. Casu, S. Triscari, S. Battiato, L. Guarnera, and P. Caponnetto, “AI Chatbots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications,” Appl. Sci., vol. 14, no. 13, p. 5889, Jul. 2024, doi: 10.3390/app14135889.

L. Labadze, M. Grigolia, and L. Machaidze, “Role of AI chatbots in education: systematic literature review,” Int. J. Educ. Technol. High. Educ., vol. 20, no. 1, p. 56, Oct. 2023, doi: 10.1186/s41239-023-00426-1.

H. Chin et al., “The Potential of Chatbots for Emotional Support and Promoting Mental Well-Being in Different Cultures: Mixed Methods Study,” J. Med. Internet Res., vol. 25, p. e51712, Oct. 2023, doi: 10.2196/51712.

L. Anindyati, “Analisis dan Perancangan Aplikasi Chatbot Menggunakan Framework Rasa dan Sistem Informasi Pemeliharaan Aplikasi (Studi Kasus: Chatbot Penerimaan Mahasiswa Baru Politeknik Astra),” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 2, pp. 291–300, Apr. 2023, doi: 10.25126/jtiik.20231026409.

A. Rachman, I. Mardhiyah, and M. Jannah, “Implementasi Chatbot FAQ pada Aplikasi Monev Kinerja Direktorat Jenderal Anggaran Menggunakan Framework Rasa Open Source,” J. KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 1, pp. 62–72, 2023, doi: 10.30865/klik.v4i1.1020.

T. Bunk, D. Varshneya, V. Vlasov, and A. Nichol, “DIET: Lightweight Language Understanding for Dialogue Systems,” arXiv Prepr., Apr. 2020, [Online]. Available: http://arxiv.org/abs/2004.09936.

C. Nixon, “Current perspectives: the impact of cyberbullying on adolescent health,” Adolesc. Health. Med. Ther., p. 143, Aug. 2014, doi: 10.2147/AHMT.S36456.

E. Menesini and C. Salmivalli, “Bullying in schools: the state of knowledge and effective interventions,” Psychol. Health Med., vol. 22, no. sup1, pp. 240–253, Mar. 2017, doi: 10.1080/13548506.2017.1279740.

H. Källmén and M. Hallgren, “Bullying at school and mental health problems among adolescents: a repeated cross-sectional study,” Child Adolesc. Psychiatry Ment. Health, vol. 15, no. 1, p. 74, Dec. 2021, doi: 10.1186/s13034-021-00425-y.

T. Zhang, A. M. Schoene, S. Ji, and S. Ananiadou, “Natural language processing applied to mental illness detection: a narrative review,” npj Digit. Med., vol. 5, no. 1, p. 46, Apr. 2022, doi: 10.1038/s41746-022-00589-7.

P. Smutny and P. Schreiberova, “Chatbots for learning: A review of educational chatbots for the Facebook Messenger,” Comput. Educ., vol. 151, p. 103862, Jul. 2020, doi: 10.1016/j.compedu.2020.103862.

B. Kang and M. Hong, “Development and Evaluation of a Mental Health Chatbot Using ChatGPT 4.0: Mixed Methods User Experience Study With Korean Users,” JMIR Med. Informatics, vol. 13, p. e63538, Jan. 2025, doi: 10.2196/63538.

S. Chancellor and M. De Choudhury, “Methods in predictive techniques for mental health status on social media: a critical review,” npj Digit. Med., vol. 3, no. 1, p. 43, Mar. 2020, doi: 10.1038/s41746-020-0233-7.

K.-J. Oh, D. Lee, B. Ko, and H.-J. Choi, “A Chatbot for Psychiatric Counseling in Mental Healthcare Service Based on Emotional Dialogue Analysis and Sentence Generation,” in 2017 18th IEEE International Conference on Mobile Data Management (MDM), May 2017, pp. 371–375, doi: 10.1109/MDM.2017.64.

V. Kumari, C. Gosavi, Y. Sharma, and L. Goel, “Domain-Specific Chatbot Development Using the Deep Learning-Based RASA Framework,” in Communication and Intelligent Systems, 2022, pp. 883–896.

I. K. R. Arthana, L. J. E. Dewi, K. A. Seputra, and N. W. Marti, “Undiksha Virtual Assistant (SHAVIRA): Integration Frequency Asked Question with Rasa Framework,” JST (Jurnal Sains dan Teknol., vol. 10, no. 2, pp. 264–273, Nov. 2021, doi: 10.23887/jstundiksha.v10i2.39863.

T. Bocklisch, J. Faulkner, N. Pawlowski, and A. Nichol, “Rasa: Open Source Language Understanding and Dialogue Management,” arXiv Prepr., Dec. 2017, [Online]. Available: http://arxiv.org/abs/1712.05181.

A. Oguntimilehin et al., “Mental Health Chatbot Using Deep Learning and Natural Language Processing,” in 2024 IEEE 5th International Conference on Electro-Computing Technologies for Humanity (NIGERCON), Nov. 2024, pp. 1–5, doi: 10.1109/NIGERCON62786.2024.10927008.

X. Liu, A. Eshghi, P. Swietojanski, and V. Rieser, “Benchmarking Natural Language Understanding Services for building Conversational Agents,” arXiv Prepr., Mar. 2019, doi: 1903.05566.

Y. Yang, J. Tavares, and T. Oliveira, “A New Research Model for Artificial Intelligence–Based Well-Being Chatbot Engagement: Survey Study,” JMIR Hum. Factors, vol. 11, p. e59908, Nov. 2024, doi: 10.2196/59908.

H. P. D and V. V, “Intelligent Chatbot Development for Text based Cyberbullying Prevention,” Int. J. New Innov. Eng. Technol., vol. 17, no. 1, pp. 73–81, 2021.

N. Sulisrudatin, “Kasus Bullying Dalam Kalangan Pelajar (Suatu Tinjauan Kriminologi),” J. Ilm. Huk. Dirgant., vol. 5, no. 2, Jun. 2014, doi: 10.35968/jh.v5i2.109.

T. A. Zuraiyah, D. K. Utami, and D. Herlambang, “Implementasi Chatbot Pada Pendaftaran Mahasiswa Baru Menggunakan Recurrent Neural Network,” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 2, pp. 91–101, Apr. 2019, doi: 10.35760/tr.2019.v24i2.2388.

V. Kumari, A. Jain, Y. Sharma, and L. Goel, “Conversational Question Answering System using RASA Framework,” in Applications of Machine Intelligence in Engineering, New York: CRC Press, 2022, pp. 489–498.

E. Young Oh, D. Song, and H. Hong, “Interactive Computing Technology in Anti-Bullying Education: The Effects of Conversation-Bot’s Role on K-12 Students’ Attitude Change Toward Bullying Problems,” J. Educ. Comput. Res., vol. 58, no. 1, pp. 200–219, Mar. 2020, doi: 10.1177/0735633119839177.

D. L. S. G. Piccolo and P. H. Alani, “Children’s Online Safety: Prospecting Chatbots for Tackling Online Abuse,” Knownledge Media Inst., no. June, 2020.

M. Franchini et al., “Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine,” Int. J. Environ. Res. Public Health, vol. 17, no. 23, p. 8786, Nov. 2020, doi: 10.3390/ijerph17238786.

S. HV and S. S, “Implementation of an Educational Chatbot using Rasa Framework,” Int. J. Innov. Technol. Explor. Eng., vol. 11, no. 9, pp. 29–35, Aug. 2022, doi: 10.35940/ijitee.G9189.0811922.

T. Maulida et al., “Visualization of Front-End Data Logger Internet of Things Technology using Vue.Js Framework,” in 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Dec. 2022, pp. 693–698, doi: 10.1109/ICITISEE57756.2022.10057919.

A. N. A. Zumaroh et al., “Development of Application Programming Interface (Api) for Amikom Purwokerto Handsanitizer (Ampuh) Data Logger Visualization,” J. Tek. Inform., vol. 3, no. 3, pp. 791–796, 2022, [Online]. Available: http://jutif.if.unsoed.ac.id/index.php/jurnal/article/view/222.

M. Mahmud et al., “Implementation of C5.0 Algorithm using Chi-Square Feature Selection for Early Detection of Hepatitis C Disease,” J. Electron. Electromed. Eng. Med. Informatics, vol. 6, no. 2, pp. 116–124, Mar. 2024, doi: 10.35882/jeeemi.v6i2.384.

I. M. Putra, I. Tahyudin, H. A. A. Rozaq, A. Y. Syafa’At, R. Wahyudi, and E. Winarto, “Classification analysis of COVID19 patient data at government hospital of banyumas using machine learning,” in 2021 2nd International Conference on Smart Computing and Electronic Enterprise: Ubiquitous, Adaptive, and Sustainable Computing Solutions for New Normal, ICSCEE 2021, Jun. 2021, pp. 271–274, doi: 10.1109/ICSCEE50312.2021.9498020.