Developing an Industry-Based DevOps Learning Framework Through Microservices, CI/CD, and AI-Driven Bus Scheduling Optimization for Vocational Education

Roni Andarsyah; Cahyo Prianto; Dodi Permadi

doi:10.17509/jmee.v13i2.98622

Developing an Industry-Based DevOps Learning Framework Through Microservices, CI/CD, and AI-Driven Bus Scheduling Optimization for Vocational Education

Roni Andarsyah, Cahyo Prianto, Dodi Permadi

Abstract

The digital transformation within the public transportation industry demands information systems that are responsive, modular, and capable of supporting data-driven decision-making. This research integrates two previously independent strategic approaches: (1) a DevOps architecture utilizing a CI/CD Pipeline based on Microservices, enhanced with API Logging and centralized monitoring to support the ticketing Dashboard system for bus operators, and (2) a hybrid optimization approach combining Machine Learning and Genetic Algorithms (GA) to optimize airport bus scheduling. Beyond these technical contributions, this research develops an Industry-Based DevOps Learning Framework, a training-oriented framework that can be adapted for vocational education and industry-based learning in DevOps and data-driven optimization. The implementation of CI/CD and Microservices enables rapid, scalable, and zero-downtime deployment, while centralized monitoring using Elasticsearch, Logstash, Kibana (ELK) Stack and Prometheus–Grafana improves Observability and early anomaly detection. On the operational side, a LightGBM model is employed to predict the probability of passenger occupancy (load factor ≥ 70%), and these predictions are used as a fitness function within the GA to generate optimal schedules. Experimental results show significant improvements: total estimated passengers increased from 478 to 841, and the average load factor improved from 82% to 87%. Furthermore, the proposed integrated system was validated as a project-based training platform through a pilot training involving 22 vocational students, who acquired hands-on competencies in Microservices development, CI/CD Pipeline, and intelligent optimization within a real-world transportation context, showing statistically significant improvement across all six competency domains (Wilcoxon signed-rank test, p < 0.001) with large effect sizes (r = 0.78-0.94). The integration of these two approaches results in a reliable, adaptive ticketing and scheduling system capable of supporting real-time operational decision-making for bus companies.

Keywords

CI/CD; Genetic Algorithm; LightGBM; Machine Learning; Microservices; Observability; Ticketing System, Vocational Training

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DOI: https://doi.org/10.17509/jmee.v13i2.98622