Perancangan Failure-Aware Self-Healing Workflow pada Sistem Event-Driven untuk Pemulihan Proses Layanan Digital Secara Otomatis

Digital service systems built on event-drivenarchitecture relied on infrastructure-level self-healing, leaving workflow-layer failures handled reactively and processes abandoned mid-execution even when infrastructure remained available. This study designed a failure-aware self-healingworkflowthat detected, classified, and recovered digital service failures automatically based on eventcontext, treating classification as mandatory before recovery. A prototype was built following the Prototyping Model, integrating n8n as workfloworchestratorand Apache Kafka as eventbrokerinside Docker, with recovery governed by a JavaScript finite statemachine, tested on five failure scenarios and two baselineconditions, each run for 30 iterations. The system classified all failures correctly across 150 iterations, reaching 100% recovery accuracyand a 100% success ratewhere fallbackwas available. Self-healinglowered Recovery Timeby 29.90% against a no-recovery baseline, though a static-recovery baselinereached a lower time with 0% success, showing recovery speed and correctness can move in opposite directions.