Penerapan Microservices dengan Docker Swarm pada Sistem Informasi Manajemen Rumah Sakit (SIMRS): Strategi Migrasi Monolith dan Evaluasi Performa
Original Full-Text Article
Download published version for reading and archivingAbstract
Digital transformation in Indonesia’s healthcare sector demands a Hospital Information System (SIMRS) that is scalable, reliable, and easy to integrate. Most existing SIMRS still rely on a monolithic architecture, making them vulnerable to single points of failure, hard to scale per module, and prone to downtime during updates. This study aims to examine strategies for migrating monolithic architectures to microservices and to evaluate the performance of a Docker- and Docker Swarm-based deployment on a SIMRS prototype consisting of twelve functional modules. The method combines a Systematic Literature Review on recent decomposition patterns with an experimental study on a two-node cluster running fourteen stacks and thirty-five services for a total of seventy-six active containers. Measurements show that the average HTTP response time ranges from 0.6 to 5.1 milliseconds for frontends (with a single billing-frontend outlier of 82.4 ms attributable to memory saturation) and from 0.8 to 3.0 milliseconds for backends, with per-container CPU utilization mostly below two percent and stable memory consumption within allocated limits. Docker Swarm proves effective in supporting high-availability replication, rolling updates, and automatic self-healing under normal operating conditions; one observed rolling-update failure (billing-backend dropping to 0/2 replicas until manual intervention) shows that reliable image-level health checks are a prerequisite for the promised zero-downtime behavior. The study concludes that microservices with Docker Swarm are a feasible adoption strategy for SIMRS in Indonesia, with manageable operational trade-offs.
Author Biographies
Magister Ilmu Komputer, Universitas Nusa Mandiri, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Magister Ilmu Komputer, Universitas Nusa Mandiri, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Magister Ilmu Komputer, Universitas Nusa Mandiri, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Magister Ilmu Komputer, Universitas Nusa Mandiri, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Magister Ilmu Komputer, Universitas Nusa Mandiri, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
How to Cite
This work is licensed under a Copyright (c) 2026 Farizal Ginanjar, Rizqia Fauziah Rachma, Wahid Diyono, Mustawi Farhan, Windu Gata .
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License .
- Share: You are free to copy, distribute, and transmit the work in any medium or format.
- Adapt: You are free to remix, transform, and build upon the work for any purpose, even commercially.
- Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
References
Total: 27 References- Ahmad, H., Treude, C., Wagner, M., & Szabo, C. (2024). Smart HPA: A resource-efficient horizontal pod auto-scaler for microservices architectures. Proceedings of the 2024 IEEE 21st International Conference on Software Architecture (ICSA).
- Al-Debagy, O., & Martinek, P. (2018). A comparative review of microservices and monolithic architectures. IEEE 18th International Symposium on Computational Intelligence and Informatics (CINTI).
- Andrade, B., Santos, S., & Silva, A. R. (2022). From monolith to microservices: Static and dynamic analysis comparison. arXiv preprint arXiv:2204.11844. https://arxiv.org/abs/2204.11844.
- Arango, C., Dernat, R., & Sanabria, J. (2017). Performance evaluation of container-based virtualization for high-performance computing environments. arXiv preprint arXiv:1709.10140. https://arxiv.org/abs/1709.10140.
- Borges, M. C., Bauer, J., Werner, S., Gebauer, M., & Tai, S. (2024). Informed and assessable observability design decisions in cloud-native microservice applications. IEEE International Conference on Software Architecture (ICSA).
Similar Articles
Articles sharing related keywords and machine learning classifications: