Arsitektur Mikrolayanan pada Platform Penilaian Risiko Kardiovaskular Menggunakan Vue.js dan Integrasi Vertex AI
DOI:
https://doi.org/10.31599/gxcnsw60Keywords:
Black Box Testing, Heart Disease, Microservices Architecture, Vertex AI, Vue.jsAbstract
Heart disease is a leading cause of death in Indonesia, exacerbated by delayed diagnosis and lack of access to healthcare facilities. This study aimed to develop JantungIn, a cardiovascular risk assessment web platform using a microservices architecture. The system separated the user interface layer using Vue.js, server logic with Hapi.js, and a Supabase relational database. The novelty of this research lay in the real-time integration of a TensorFlow.js prediction model and a Vertex AI Dialogflow CX intelligent virtual assistant. System evaluation using the Black Box method covered positive and negative scenarios on core functionalities, including role authentication for doctors and patients, medical parameter validation, and medical history storage. The testing results showed that the software architecture successfully processed data exchange without functional errors. In conclusion, JantungIn was successfully implemented as a reliable medical decision support system to facilitate early detection of heart disease in the community.
Downloads
References
Axza, F., Sofi’ie, F., & Qoiriah, A. (2023). Analisis Perbandingan Framework Front-End Javascript React dan Vue Pada Pengembangan Website. Journal of Informatics and Computer Science, 05.
Haryanto, I. D., & Saefurrahman, S. (2024). Implementasi Chatbot Kesehatan Kucing Melalui Dialogflow dan Telegram untuk Pemberian Informasi Penyakit dan Perawatan. JTIM : Jurnal Teknologi Informasi Dan Multimedia, 5(4), 365–376. https://doi.org/10.35746/jtim.v5i4.484
Kemenkes RI. (2018). Laporan Riskesdas 2018 Nasional. https://repository.badankebijakan.kemkes.go.id/id/eprint/3514/1/Laporan Riskesdas 2018 Nasional.pdf
Kotadiya, U., Arora, A. S., & Yachamaneni, T. (2024). Intelligent Orchestration of Cloud-Native Applications Using Google Cloud Platform and Microservices-Based Architectures. International Journal of AI, BigData, Computational and Management Studies, 5. https://doi.org/10.63282/3050-9416.ijaibdcms-v5i4p111
Kustanto, P., Bram Khalil, R., & Noe’man, A. (2024). Penerapan Metode Prototype dalam Perancangan Media Pembelajaran Interaktif. Journal of Students‘ Research in Computer Science, 5(1), 83–94. https://doi.org/10.31599/6x0dfz47
Murni, I. K., Wirawan, M. T., Patmasari, L., Sativa, E. R., Arafuri, N., Nugroho, S., & Noormanto. (2021). Delayed diagnosis in children with congenital heart disease: a mixed-method study. BMC Pediatrics, 21(1). https://doi.org/10.1186/s12887-021-02667-3
Noe’man, A., Hidayat, A., Yogaswara, N., Handayani, D., Kustanto, P., & Hartanti, D. (2025). Implementasi algoritma random forest dalam sistem seleksi karyawan terbaik untuk meningkatkan efektivitas keputusan di PT. XYZ. Jurnal Manajamen Informatika Jayakarta, 5(3), 263. https://doi.org/10.52362/jmijayakarta.v5i3.2081
Qiu, M. (2025). ExtJS-JSP-Vue.js 3 Hybrid Architecture: A Case Study in Enterprise Web Application Development. 4, 2025. www.h-tsp.com
Salehi, S. S., Saadatfar, H., Oyelere, S. S., Hussain, S., Hassannataj Joloudari, J., Taheri Ledari, M., Arslan, E., & Barzegar, B. (2026). Enhancing healthcare outcome with scalable processing and predictive analytics via cloud healthcare API. Frontiers in Digital Health, 7. https://doi.org/10.3389/fdgth.2025.1687131
Santos, L. F. C. Dos, Silva, M. V. S., Santos, S. R. R. Dos, Rocha, F. G., & Silva, E. B. Da. (2024). Microfront-End: Systematic Mapping. International Conference on Web Information Systems and Technologies, WEBIST - Proceedings, 119–130. https://doi.org/10.5220/0013015400003825
Sutara, B., & Gunawan, S. S. (2024). Comparative analysis of REST API performance between Express.js framework and Hapi.js using Apache JMeter. Jurnal Riset Teknik Informatika (JURETI), 1(1), 19–26.










_-_Copy1.jpg)
