IMPLEMENTATION OF WEB-BASED AND MACHINE LEARNING DECISION SUPPORT SYSTEM "BUNDA DINI" FOR MATERNAL HEALTH RISK DETECTION
DOI:
https://doi.org/10.47701/jd3szx05Keywords:
Bunda Dini, Decision Support System, Laravel, Maternal Health, Machine LearningAbstract
Maternal Mortality Ratio (MMR) remains a strategic issue in Indonesia's national health system. Efforts to reduce MMR are often hindered by the slow early detection of high-risk pregnant women at primary healthcare facilities. To bridge this gap, this study developed "Bunda Dini", a web-based Decision Support System (DSS) integrating a predictive machine learning model. The application was built using the framework Laravel and MySQL database management system. The system architecture was designed with a multi-tenant approach, enabling secure data isolation between various clinics or hospitals, as well as structured access permissions for Midwives, Obstetricians, Clinic Admins, and Super Admins (Developers). The system's inference engine is powered by an optimized Extreme Gradient Boosting (XGBoost) algorithm, which has been proven capable of classifying pregnancy risk levels based on six vital parameters with an accuracy rate of 84.24% and a high-risk class Recall reaching 96.36%. Key features of the application include an intelligent anamnesis form, risk level visualization real-time, as well as a comprehensive reporting module supporting patient data recapitulation in both PDF and Excel formats. Test results show that the system interface is highly responsive, easy for medical staff to use, and guarantees the confidentiality of medical records across clinics. The implementation of the "Bunda Dini" web platform has high potential to become a reliable medical triage instrument, accelerating emergency obstetric care responses, and directly contributing to efforts to reduce maternal mortality.
References
Chen, W., Yang, K., Yu, Z., Shi, Y., & Chen, C. L. P. (2024). A survey on imbalanced learning : latest research , applications and future directions (Vol. 123). Artificial Intelligence Review. https://doi.org/10.1007/s10462-024-10759-6
Chevalier, D., & Côté, M. (2025). From point to probabilistic gradient boosting for claim frequency and severity prediction. European Actuarial Journal. https://doi.org/10.1007/s13385-025-00428-5
Cockburn, N., Osborne, C., Withana, S., Elsmore, A., Nanjappa, R., South, M., Parry-smith, W., & Taylor, B. (2024). Articles Clinical decision support systems for maternity care : a systematic review and meta-analysis. EClinicalMedicine, 76, 102822. https://doi.org/10.1016/j.eclinm.2024.102822
Derksen, C., Walter, F. M., Akbar, A. B., Parmar, A. V. E., Saunders, T. S., Round, T., Rubin, G., & Scott, S. E. (2025). The implementation challenge of computerised clinical decision support systems for the detection of disease in primary care : systematic review and recommendations. Implementation Science. https://doi.org/https://doi.org/10.1186/s13012-025-01445-4
Du, Y., Mcnestry, C., Wei, L., Markella, A., Mcauliffe, F. M., & Mooney, C. (2023). International Journal of Medical Informatics Machine learning-based clinical decision support systems for pregnancy care : A systematic review. International Journal of Medical Informatics, 173(July 2022), 105040. https://doi.org/10.1016/j.ijmedinf.2023.105040
Group, W. B. (2020). Trends in maternal mortality 2000 to 2020.
Hamidah, U. F. (2024). Evaluasi Literatur Penggunaan Teknologi Blockchain dalam Sistem Terdistribusi untuk Pelayanan Kesehatan. 574–578.
Jajang Jaya Purnama, Nina Kurnia Hikmawati, S. R. (2024). Analisis Algoritma Klasifikasi Untuk Mengidentifikasi Potensi Risiko Kesehatan Ibu Hamil. Jurnal ISAS, 5(1), 120–127. https://doi.org/https://doi.org/10.52158/jacost.v5i1.809
Mondal, S., Nag, A., Barman, A. K., & Karmakar, M. (2023). Machine learning-based maternal health risk prediction model for IoMT framework. 32, 145–159.
Mosca, M., Mosca, R., & Braggio, M. (2025). ScienceDirect Big Data and AI for Smart Maintenance : Literature review on the impact on plants Resilience. Procedia Computer Science, 253, 1959–1971. https://doi.org/10.1016/j.procs.2025.01.258
Murtiningsih, D. A., Sari, B. W., & Fajri, I. N. (2025). Comparison of Light Gradient Boosting Machine , eXtreme Gradient Boosting , and CatBoost with Balancing and Hyperparameter Tuning for Hypertension Risk Prediction on Clinical Dataset. JAIC, 9(5), 2753–2763. https://doi.org/https://doi.org/10.30871/jaic.v9i5.10400
Niu, S., Yin, Q., Ma, J., Song, Y., Xu, Y., Bai, L., Pan, W., & Yang, X. (2024). Enhancing healthcare decision support through explainable AI models for risk prediction. Decision Support Systems, 181(April), 114228. https://doi.org/10.1016/j.dss.2024.114228
Pambudi, A., & Srirahayu, A. (2020). Aplikasi kesehatan ibu hamil berbasis Android. Infokes, 10(2), 55–62. https://doi.org/https://doi.org/10.47701/infokes.v10i2.1034
Rahagiyanto, A., Prakoso, B. H., Yunus, M., Vestine, V., Eko, G., Suyoso, J., & Deharja, A. (2025). Perbandingan Kinerja Algoritma KNN-DT-RF-SVM untuk Deteksi Dini Risiko Kematian Ibu. 6(2), 137–145. https://doi.org/10.25047/j-remi.v6i2.5658
Salim, S., Mashrafi, A., & Tafakori, L. (2024). Predicting maternal risk level using machine learning models. BMC Pregnancy and Childbirth. https://doi.org/10.1186/s12884-024-07030-9
Sari, A. A., & Pramono. (2024). Penerapan Algoritma Deep Belief Networks (DBNs) Untuk Prediksi Kanker Serviks. Duta.Com, 17, 50–57. https://doi.org/https://doi.org/10.47701/dutacom.v17i1.3790
Straw, I., Brass, I., & Mkwashi, A. (n.d.). Insights From a Clinically Orientated Workshop on Health Care Cybersecurity and Medical Technology : Observational Study and Thematic Analysis Corresponding Author : 26, 1–11. https://doi.org/10.2196/50505
Tan, Y., Liu, X., Yang, Y., & Li, B. (2023). Metabolomics analysis reveals serum biomarkers in patients with diabetic sarcopenia. March, 1–13. https://doi.org/10.3389/fendo.2023.1119782
Vemulapalli, H. S., Iyengar, S. K., Ko, N. L. K., Iyengar, S., Saranathan, S., & Srivathsan, K. (2024). Navigating the diagnostic challenges of the accessory pathway : Participant or bystander ? Heart Rhythm Case Reports, 10(5), 335–340. https://doi.org/10.1016/j.hrcr.2024.02.011
Villar, A., Robledo, C., & Andrade, V. De. (2024). Supervised machine learning algorithms for predicting student dropout and academic success : a comparative study. Discover Artificial Intelligence. https://doi.org/10.1007/s44163-023-00079-z
Virginia Khoirunnisa, S. L. (2023). Implementasi Klasifikasi Kehamilan Beresiko dengan Metode Naive Bayes pada Puskermas Kelurahan Malaka Jaya. Jurnal Manajemen Informatika Dan Komunikasi, 4(3), 1680–1693. https://doi.org/https://doi.org/10.35870/jimik.v4i3.396
