Pengembangan Aplikasi Mobile Manajemen Servis Kendaraan Offline Menggunakan Metode Waterfall
DOI:
https://doi.org/10.21009/pinter.10.1.4Keywords:
flutter network, mobile aplication, waterfall methodAbstract
The management of motor vehicle data, such as service history, fuel consumption, and tax administration, is still done manually in many workshops, which risks causing data loss, recording errors, and irregularities in vehicle maintenance monitoring. This condition indicates the need for a digital system that can help customers and workshop staff manage vehicle information in a more structured and easily accessible manner. This research aims to develop an Offline Vehicle Service Management Mobile Application as a digital solution to support vehicle data management at Adit Garage Workshop. The application development uses the Waterfall method within the Software Development Life Cycle (SDLC) framework, which includes the stages of requirements analysis, system design, implementation, and testing. The application is developed using the Flutter framework and designed to operate fully offline without relying on an API or backend server, so vehicle data can still be managed when an internet connection is not available. The main features developed include recording vehicle identities, digitizing service histories, recording fuel consumption, and reminders for vehicle tax and Vehicle Registration Certificate (STNK) expiration. The development results show that the application is capable of integrating various vehicle maintenance information into a simple and user-friendly mobile platform. Testing using the Black Box Testing method shows that all tested features can operate according to their designed functions without any functional errors found. The implementation of the application also provides ease in recording, searching, and monitoring vehicle information for both customers and workshop personnel. Thus, the developed application can serve as an efficient digital solution to support the management and monitoring of vehicle maintenance, especially in workshop environments that require a system capable of operating without an internet connection.
References
References
Abreu, R., Branco, F., Reis, M. J. C. S., & Serôdio, C. (2025). Cybersecurity in connected and autonomous vehicles: A systematic review of automotive security. IEEE Access, 13, 116818–116855. https://doi.org/10.1109/access.2025.3584649
Arandia, N., Garate, J. I., & Mabe, J. (2023). Medical devices with embedded sensor systems: Design and development methodology for start-ups. Sensors, 23(5), 2578. https://doi.org/10.3390/s23052578
Bansal, D., Sinha, D., Sinha, D., & Kumar Khandelwal, S. (2026). Design and implementation of a multiuser enterprise resource planning solution for higher education institutions: Enhancing accreditation and administration. Software: Practice and Experience, 56(5), 551–587. https://doi.org/10.1002/spe.70060
Carling, K., Paidi, V., & Rudholm, N. (2025). On deploying eCOmpass: A decision support tool for environmentally friendly retail locations. International Journal of Engineering Business Management, 17. https://doi.org/10.1177/18479790251356663
Di Leo, S., De Cicco, L., & Mascolo, S. (2025). Real-time speech-to-text on edge: A prototype system for ultra-low latency communication with AI-Powered NLP. Information, 16(8), 685. https://doi.org/10.3390/info16080685
Hassan, M. K., Mohd Rusli, M. H., Kayat, S., & Wan Mokhtar, W. M. A. (2025). Enhancing spare parts inventory control in automotive SMEs: A digital approach with Google Tools Integration. International Journal of Integrated Engineering, 17(8). https://doi.org/10.30880/ijie.2025.17.08.011
Husain, E., Patel, D., Kore, V., Gupta, M., & Sharmiladevi, S. (2026). S-edge: A multi-region edge computing framework with adaptive data compression and dynamic load balancing. IEEE Access, 14, 81645–81664. https://doi.org/10.1109/access.2026.3696637
Ianculescu, M., Constantin, V.-Ș., Gușatu, A.-M., Petrache, M.-C., Mihăescu, A.-G., Bica, O., & Alexandru, A. (2025). Enhancing connected health ecosystems through IoT-Enabled monitoring technologies: A case study of the Monit4Healthy system. Sensors, 25(7), 2292. https://doi.org/10.3390/s25072292
Kavitha, A., R, N. M., Vikkas, P. S. D., R, V., & T, R. (2026). Digital platform for vehicle service booking and monitoring. 2026 International Conference on Communication, Computing and Emerging Technologies (IC3ET), 1–10. https://doi.org/10.1109/ic3et64989.2026.11467376
Kim, H., Zhao, Y., Pavlo, A., & Gibbons, P. B. (2025). No cap, this memory slaps: Breaking through the memory wall of transactional database systems with processing-in-memory. Proceedings of the VLDB Endowment, 18(11), 4241–4254. https://doi.org/10.14778/3749646.3749690
Ku, D., Zang, H., Yusupov, A., Park, S., & Kim, J. (2025). Vehicle-to-everything-car edge cloud management with development, security, and operations automation framework. Electronics, 14(3), 478–478. https://doi.org/10.3390/electronics14030478
Kurisaka, H., Su, Y., Nguyen, P. Le, Nguyen, K., & Sekiya, H. (2025). Performance evaluation of ethereum consensus mechanisms in IoT-blockchain systems using resource-constrained devices. Cluster Computing, 28(12). https://doi.org/10.1007/s10586-025-05503-w
Sielskait˙e, Š., & Kalibatienė, D. (2025). The impact of human factors on software development processes applying the agile and waterfall methodologies: A case study using real data. International Journal of Computers Communications & Control, 20(4). https://doi.org/10.15837/ijccc.2025.4.6807
Veerapaneni, R. K., Delhibabu, R., Subbotin, A., & Zhukova, N. (2026). Development of a high-performance in-memory database architecture for intelligent video surveillance in critical patient care. Frontiers in Digital Health, 8. https://doi.org/10.3389/fdgth.2026.1807507
Zeydan, E., Arslan, S., & Turk, Y. (2026). A cloud native journey for telecommunication networks: Components, applications and open challenges. ACM Computing Surveys, 58(10), 1–38. https://doi.org/10.1145/3801492
