Perancangan Basis Data Knowledge Base Berbasis Relasional untuk Mendukung AI Agent pada Sistem Layanan Informasi
DOI:
https://doi.org/10.70404/jikteks.v4i03.706Keywords:
AI agent, Basis data relasional, Database life cycle, Knowledge base, Large language modelAbstract
The rapid advancement of Artificial Intelligence (AI), particularly Large Language Models (LLMs), has accelerated the adoption of AI Agents in various information service systems. However, the quality of responses generated by AI Agents depends not only on language models but also on the structure and management of the underlying knowledge source. Most previous studies have focused on AI Agent implementation and API integration, while limited attention has been given to the design of relational knowledge base databases. This study aims to design a relational knowledge base database that supports AI Agents in information service systems. The research adopts the Database Life Cycle (DBLC) methodology, including data requirement analysis, entity identification, conceptual database design, normalization, logical database design, physical database implementation, and database testing. The proposed database consists of several core entities, including information categories, knowledge repository, metadata, users, administrators, conversation history, user feedback, and AI processing logs. The resulting schema satisfies Third Normal Form (3NF), reducing data redundancy while improving consistency, scalability, and maintainability. Furthermore, the proposed database supports AI Agent integration by providing structured retrieval mechanisms for contextual information before being processed by Large Language Models. The proposed relational database architecture contributes as a scalable foundation for developing AI Agent-based information service systems across various application domains.
Downloads
References
T. Connolly and C. Begg, Database Systems: A Practical Approach to Design, Implementation, and Management, 6th ed. London, United Kingdom: Pearson Education, 2015.
R. Elmasri and S. B. Navathe, Fundamentals of Database Systems, 7th ed. Boston, MA, USA: Pearson, 2017.
A. Silberschatz, H. F. Korth, and S. Sudarshan, Database System Concepts, 7th ed. New York, NY, USA: McGraw-Hill Education, 2019.
C. J. Date, Database Design and Relational Theory: Normal Forms and All That Jazz, 2nd ed. Sebastopol, CA, USA: O'Reilly Media, 2019.
C. Coronel and S. Morris, Database Systems: Design, Implementation, and Management, 13th ed. Boston, MA, USA: Cengage Learning, 2019.
I. Greguric, I. Tomičić, and M. Kusek, "Conversational AI: State of the Art, Applications, and Challenges," IEEE Access, vol. 9, pp. 120364–120381, 2021, doi: 10.1109/ACCESS.2021.3111220.
L. Wang et al., "A Survey on Large Language Model Based Autonomous Agents," arXiv preprint, arXiv:2308.11432, 2023. Available: https://arxiv.org/abs/2308.11432
H. Ding et al., "Evaluation Framework for Conversational Agents with Artificial Intelligence in Health Interventions: A Systematic Scoping Review," Journal of the American Medical Informatics Association, vol. 31, no. 3, pp. 746–761, 2024, doi: 10.1093/jamia/ocad222.
L. T. Car et al., "Conversational Agents in Healthcare: A Scoping Review and Conceptual Analysis," Journal of Medical Internet Research, vol. 22, no. 8, Art. no. e17158, 2020, doi: 10.2196/17158.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks," Advances in Neural Information Processing Systems, vol. 33, pp. 9459–9474, 2020. Available: https://arxiv.org/abs/2005.11401
P. Zhao, H. Zhang, Q. Yu, Z. Wang, Y. Geng, F. Fu, L. Yang, W. Zhang, and B. Cui, "Retrieval-Augmented Generation for AI-Generated Content: A Survey," arXiv preprint, arXiv:2312.10997, 2024. Available: https://arxiv.org/abs/2312.10997
J. James, M. Trovati, and A. Bolton, "Retrieval-Augmented Generation to Generate Knowledge Assets and Creation of Action Drivers," Applied Sciences, vol. 15, no. 11, Art. no. 6247, 2025, doi: 10.3390/app15116247.
M. Murtiyoso, I. Tahyudin, and B. Berlilana, "A Systematic Review of Retrieval-Augmented Generation for Enhancing Domain-Specific Knowledge in Large Language Models," Sinkron, vol. 9, no. 2, pp. 969–977, 2025, doi: 10.33395/sinkron.v9i2.14824.
Z. Qin, C. Luo, Z. Wang, H. Jiang, and Y. Sun, "Relational Database Augmented Large Language Model," arXiv preprint, arXiv:2407.15071, 2024. Available: https://arxiv.org/abs/2407.15071
Y. Zhang, L. Chen, H. Wang, and X. Li, "Large Language Models for Intelligent Information Systems: A Review," Artificial Intelligence Review, 2023.
A. R. Hevner, S. T. March, J. Park, and S. Ram, "Design Science in Information Systems Research," MIS Quarterly, vol. 28, no. 1, pp. 75–105, 2004. Available: https://www.jstor.org/stable/25148625
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Rohmadi, Reza Widiantoro
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
