Pengujian Fungsional, Usability, dan Kinerja Dental Tutor AI sebagai Alat Bantu Belajar Periodonsia
DOI:
https://doi.org/10.36277/jteuniba.v10i2.1324Keywords:
Chatbot, Kecerdasan Buatan, Periodonsia, Usability Testing.Abstract
Penelitian ini bertujuan mengevaluasi kinerja, fungsionalitas, dan usability Dental Tutor AI, sebuah chatbot pembelajaran berbasis document-driven conversational AI yang dikembangkan menggunakan platform Chatbase dengan sumber pengetahuan utama buku Newman and Carranza’s Essentials of Clinical Periodontology. Proses pengembangan meliputi ekstraksi teks, text chunking, dan pembuatan semantic embeddings untuk memungkinkan pencarian konteks yang akurat. Pengujian dilakukan melalui tiga pendekatan: black-box testing pada delapan skenario fungsional, evaluasi usability menggunakan System Usability Scale (SUS), serta audit teknis antarmuka menggunakan Lighthouse. Hasil pengujian menunjukkan bahwa seluruh skenario fungsional dinyatakan pass, menandakan chatbot mampu menangani variasi input secara konsisten. Evaluasi SUS menghasilkan skor 90,17 yang termasuk kategori Excellent, mengindikasikan bahwa sistem mudah digunakan dan diterima dengan baik oleh pengguna. Audit Lighthouse memperlihatkan performa dan best practices yang sangat baik, meskipun aspek aksesibilitas masih memerlukan peningkatan. Secara keseluruhan, Dental Tutor AI terbukti layak dan efektif sebagai alat bantu pembelajaran digital di bidang periodonsia
Downloads
References
C. Gan, “Research on the Development of Artificial Intelligence Technology and its Application in Education,” Journal of Management and Social Development, 2025.
S. Vakayil, D. S. Juliet, A. J., and S. Vakayil, “RAG-Based LLM Chatbot Using Llama-2,” in Proc. 2024 7th Int. Conf. Devices, Circuits and Systems (ICDCS), 2024, pp. 1–5.
L. Pawlik, “How the Choice of LLM and Prompt Engineering Affects Chatbot Effectiveness,” Electronics, 2025.
B. S. Mustafa and Y. E. Madhi, “LLM and RAG Powered Chatbot for the College of Computer Science and Mathematics at the University of Mosul,” Int. Res. J. Innovations Eng. Technol., 2024.
A. LieB and T. Goel, “Student Interaction with NewtBot: An LLM-as-tutor Chatbot for Secondary Physics Education,” in Extended Abstracts of the CHI Conf. Human Factors Comput. Syst., 2024.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Kuttler, et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” 2020.
Y. Gao, Y. Xiong, X. Gao, K. Jia, J. Pan, Y. Bi, Y. Dai, J. Sun, Q. Guo, M. Wang, et al., “Retrieval-Augmented Generation for Large Language Models: A Survey,” arXiv, 2023, abs/2312.10997.
S. Zeng, J. Zhang, P. He, Y. Xing, Y. Liu, H. Xu, J. Ren, S. Wang, D. Yin, Y. Chang, and J. Tang, “The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG),” in Proc. Annu. Meeting Assoc. Comput. Linguist., 2024.
N. Mayat, C. Wachter, S. Spatzenegger, M. P. Hinrichs, T. Weißer, and R. H. Schmitt, “Performance of RAG-Based Systems in Industrial Organizations: A Case Study in the Automotive Industry,” in Proc. 2025 IEEE 8th Int. Conf. Industrial Cyber-Physical Systems (ICPS), 2025, pp. 1–6.
S. Kumar, D. Paikar, K. S. Vutukuri, H. Ali, S. R. Ainala, A. M. Krishnan, and Y. Zhang, “KatzBot: Revolutionizing Academic Chatbot for Enhanced Communication,” arXiv, 2024, abs/2410.16385.
D. H. Patel, D. V. Vemuri, D. J. Kaur, G. M. Kumar, D. T. Moharekar, and D. M. Chakravarthi, “Enhancing Student Support and Engagement with Natural Language Processing in Academic Chatbot's,” in Proc. 2023 10th IEEE Uttar Pradesh Section Int. Conf. Electrical, Electronics and Computer Engineering (UPCON), 2023, pp. 354–358.
M. Špeletić, M. Protić, and S. Nikolić, “Exploring RAG in Medical Question Answering: Integrating LLMs and Vector Databases,” UNITECH – Selected Papers, 2024.
A. M. Deshmukh and R. Chalmeta, “Validation of system usability scale as a usability metric to evaluate voice user interfaces,” PeerJ Comput. Sci., vol. 10, 2024.
T. McGill, O. Bamgboye, X. Liu, and C. S. Kalutharage, “Towards Improving Accessibility of Web Auditing with Google Lighthouse,” in Proc. 2023 IEEE 47th Annu. Comput., Softw., Appl. Conf. (COMPSAC), 2023, pp. 1594–1599.
E. Ogbuju, B. Ayodeji, and A. Azeez, “Performance and accessibility evaluation of university websites in Nigeria,” IEEE Int. Conf. on Education and Development, 2022.
A. S. Asrese, S. J. Eravuchira, and V. Bajpai, “Measuring web latency and rendering performance: Method, tools, and longitudinal dataset,” in Proc. IEEE/IFIP Network Operations and Management Symposium (NOMS), 2019.
P. Mathew, “Front-end performance optimization for next-generation digital services,” Journal of Computer Science and Technology Studies, vol. 7, no. 1, 2025.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Yusrini Selviani, Nur Rahmah Hasanuddin, muhammad takdir muslihi, Alvina Damayanti
This work is licensed under a Creative Commons Attribution 4.0 International License.
