Pengembangan Chatbot Deteksi Dini Diabetes Melitus Tipe 2 Berbasis Algoritma Random Forest
DOI:
https://doi.org/10.55316/hm.v17i2.1130Keywords:
Chatbot, Type 2 Diabetes Mellitus, Random Forest AlgorithmAbstract
Type 2 Diabetes Mellitus (T2DM) stands as one of the leading non-communicable diseases globally and significantly contributes to early mortality due to its complications. Early identification of high-risk individuals is essential in alleviating the burden of this disease. This study aimed to develop an early detection chatbot for T2DM based on machine learning using the Random Forest algorithm integrated into the Telegram platform. The research employed a Research and Development (R&D) approach following the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The dataset used was the Pima Indians Diabetes dataset, which includes eight key health variables. The model was trained and tested to predict T2DM risk and implemented in an interactive chatbot developed using Python and the Telegram Bot API. The training results indicate that the Random Forest model achieved good performance with an accuracy of 85%, precision of 83%, and recall of 80%, demonstrating the model’s reliable and sensitive capability to detect diabetes risk. The chatbot effectively collects user health data through simple conversational interactions and provides real-time prediction results. System evaluation through User Acceptance Testing (UAT) involving 35 respondents showed a very positive response, with an average score of 4.5 out of 5, indicating that the chatbot is easy to use, informative, and beneficial as an educational and early screening tool for non-communicable diseases. Recommendations for future research include using localized datasets to better represent the Indonesian population and integrating the chatbot with Electronic Medical Records (EMR) systems to enable direct clinical application by healthcare professionals
References
AlMakinah, R. (2025). Factors That Influence the Adoption of AI-enabled Conversational Agents (AICAs) as an Augmenting Therapeutic Tool by Frontline Healthcare Workers: From Technology Acceptance Model 3 (TAM3) Lens--A Systematic Mapping Review. arXiv preprint arXiv:2504.13183.
Berry, L. L. (1995). Relationship marketing of services—growing interest, emerging perspectives. Journal of the Academy of marketing science, 23(4), 236-245.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Care, D. (2022). Diabetes: Standards of medical care in diabetes—2022. Diabetes Care, 45(1), S113-S124.
Chang, V., Bailey, J., Xu, Q. A., & Sun, Z. (2023). Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms. Neural Computing and Applications, 35(22), 16157-16173.
Cho, N. H., Shaw, J. E., Karuranga, S., Huang, Y., da Rocha Fernandes, J. D., Ohlrogge, A. W., & Malanda, B. I. D. F. (2018). IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes research and clinical practice, 138, 271-281.
Dua, D., & Graff, C. (2017). UCI Machine Learning Repository: Pima Indians Diabetes Dataset. University of California, Irvine. https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes
Ginting, J. B., Suci, T., Ginting, C. N., & Girsang, E. (2023). Early detection system of risk factors for diabetes mellitus type 2 utilization of machine learning-random forest. Journal of Family and Community Medicine, 30(3), 171-179.
Hennebelle, A., Materwala, H., & Ismail, L. (2023). HealthEdge: a machine learning-based smart healthcare framework for prediction of type 2 diabetes in an integrated IoT, edge, and cloud computing system. Procedia Computer Science, 220, 331-338.
International Diabetes Federation. (2021). IDF Diabetes Atlas, 10th edition. https://diabetesatlas.org/
International Organization for Standardization (ISO). (2018). Ergonomic Requirements for Office Work with Visual Display Terminals (VDTs) – Part 11: Guidance on Usability (ISO 9241-11). Geneva: ISO.
Kaur, H., & Kumari, V. (2022). Predictive modelling and analytics for diabetes using a machine learning approach. Applied computing and informatics, 18(1/2), 90-100.nform.
Kusuma, A., Wibowo, A., & Nugroho, H. (2021). “Random forest implementation in diabetes disease classification”. International Journal of Computer Applications, 176(45), 1-6.
Li, S., Tang, X., Luo, Y., Wu, B., Huang, Z., Li, Z., ... & Chen, Y. (2020). Impact of long-term glucose variability on coronary atherosclerosis progression in patients with type 2 diabetes: a 2.3 year follow-up study. Cardiovascular Diabetology, 19(1), 146..
Ng KW, Lee H, et al. (2025). AI-Powered Chatbots in Healthcare: Improving Patient Engagement and Preventive Care. Front Public Health.
Palanica, A., Flaschner, P., Thommandram, A., Li, M., & Fossat, Y. (2019). Physicians’ perceptions of chatbots in health care: cross-sectional web-based survey. Journal of medical Internet research, 21(4), e12887.
Razzak I, Imran M. (2013). AI Applications in Healthcare: Enhancing Early Detection and Patient Engagement. Comput Methods Programs Biomed.
Suryadevara, C. K. (2023). Diabetes risk assessment using machine learning: a comparative study of classification algorithms. IEJRD-International Multidisciplinary Journal, 8(4), 10.
The Guardian. (2024). NHS to begin world-first trial of AI tool to identify type 2 diabetes risk. https://www.theguardian.com/society/2024/dec/23/nhs-to-begin-world-first-trial-of-ai-tool-to-identify-type-2-diabetes-risk
Tigga, N. P., & Garg, S. (2020). Prediction of type 2 diabetes using machine learning classification methods. Procedia Computer Science, 167, 706-716.
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44-56.
Vijayarani, M., & Balamurugan, G. (2019). Chatbot in Health Care–a review. RGUHS Journal of Nursing Sciences, 9(1).
Zheng, T., Xie, W., Xu, L., He, X., Zhang, Y., You, M., ... & Chen, Y. (2017). A machine learning-based framework to identify type 2 diabetes through electronic health records. International journal of medical informatics, 97, 120-127.
