Dika, Randy Yufid and Putra, Saiful Adi (2025) Implementasi Automasi Deployment Aplikasi “RouteRush” Berbasis CI/CD pada Platform Google Cloud. Project Report (Praktek Kerja Lapang dan Magang). Fakultas Ilmu Komputer, Surabaya.
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Abstract
The logistics sector in the digital era faces major challenges in meeting the demand for fast, efficient, and reliable delivery services. The continuously increasing demand and users’ expectations for shorter delivery times require technological innovations capable of improving operational efficiency and productivity. The “Route Rush” project was developed as a technology-based solution to support optimal delivery route management through real-time data and machine learning models. The implementation of Continuous Integration/Continuous Deployment (CI/CD) on the Google Cloud platform became the primary strategy in developing this application to support the automation of development, testing, and deployment processes. The objective of this internship project was to build an application that is reliable, efficient, and responsive to user needs. The implementation of this project was carried out systematically through several stages designed to support proper application development. Starting from the planning stage, user requirements analysis was conducted to determine the application’s main features. The designed features included efficient route searching based on real-time weather and traffic data, address history storage, and user information management. In addition, Node.js together with the HAPI Framework was utilized for modular and flexible backend development, enabling more efficient service management. Machine learning models were also integrated to support delivery route optimization through accurate recommendations. During the build stage, Docker was used to create consistent containers, while Artifact Registry was utilized to store the generated container images. This build process was managed through a CI/CD pipeline provided by Cloud Build. Every change in the GitHub repository automatically triggered a rebuild process that generated a new image. Through this approach, every development iteration could be directly tested and prepared for deployment. The testing stage was conducted using Postman to ensure that every API endpoint functioned according to specifications. The testing process included data validation, API security testing to prevent unauthorized access, and performance testing to evaluate the API’s capability in handling concurrent requests. In addition, integration testing between frontend, backend, and machine learning components was performed to ensure that all application features worked according to the design. The deployment stage utilized Cloud Run, a serverless platform that supports automatic deployment. With Cloud Run, the application could be deployed efficiently because all application dependencies had previously been packaged into containers. Cloud Run also provides an autoscaling feature that automatically adjusts the number of instances based on user traffic levels. This ensures that the application remains responsive even during traffic spikes. Furthermore, this serverless approach reduces the need for manual infrastructure management, allowing the development team to focus more on developing new features. In the operational stage, Compute Engine was used to support database management, while Cloud Storage was selected to store machine learning models. Network configuration was implemented through a Virtual Private Cloud (VPC) with firewall rules to ensure that only authorized users could access the data. This cloud infrastructure design ensured security, reliability, and scalability in supporting application operations. The monitoring and control stage was carried out using Cloud Monitoring and Cloud Logging to monitor application performance in real time. Cloud Monitoring enabled the monitoring of resource usage such as CPU, memory, and network, while Cloud Logging was used to record application activities and detect potential issues. An automatic alert system was implemented to detect anomalies early and notify the development team whenever system disruptions occurred. The implementation results showed that the “Route Rush” application successfully improved efficiency in delivery route management. Through route optimization using machine learning models, travel time could be reduced and fuel consumption became more efficient. The implemented CI/CD system accelerated the application development cycle and ensured that every released version had undergone comprehensive testing. In addition, by utilizing Cloud Run services, resource management became more flexible and cost-efficient, while the autoscaling capability ensured optimal application performance even during traffic surges. This project opens opportunities for further development, such as integrating the application with third-party logistics systems or developing additional features such as batch management for delivery addresses. By adopting a modern technology-based approach, this project demonstrates that automation in software development can provide significant positive impacts on operational efficiency and user satisfaction. The success of the “Route Rush” application is expected to become an example of CI/CD technology implementation in supporting digital transformation within the logistics sector.
| Item Type: | Monograph (Project Report (Praktek Kerja Lapang dan Magang)) | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76.6 Computer Programming T Technology > T Technology (General) > T58.6-58.62 Management Information Systems |
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| Divisions: | Faculty of Computer Science > Departemen of Information Systems | ||||||||
| Depositing User: | Randy Yufid Dika | ||||||||
| Date Deposited: | 25 May 2026 08:45 | ||||||||
| Last Modified: | 26 May 2026 01:07 | ||||||||
| URI: | https://repository.upnjatim.ac.id/id/eprint/52254 |
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