Development of a Mobile Smart Fridge System for Intelligent Food Stock Monitoring and Management
Keywords:
Smart Fridge, Android, Manajemen Stok, Rekomendasi Resep, APIAbstract
Household food waste remains a critical sustainability challenge, largely driven by inadequate inventory management and limited awareness of food expiration timelines. This study develops and evaluates an Android-based Smart Fridge application designed to improve household food stock management and reduce food waste. The research employs a Research and Development (R&D) approach integrated with the Technology Acceptance Model (TAM) and Technology Readiness Level (TRL) assessment. The application incorporates core features including inventory recording (CRUD system), expiration date tracking, minimum stock alerts, and automated notifications to support informed consumption decisions. Field testing was conducted with 35 households over a 30-day period using a pre-test and post-test design. Results indicate a statistically significant reduction in average weekly food waste (approximately 40%), accompanied by decreased over-purchasing frequency and improved expenditure efficiency. The effect size analysis demonstrates a substantial behavioral impact. TAM evaluation reveals high levels of perceived usefulness and perceived ease of use, both significantly influencing users’ behavioral intention to continue using the application. Reliability testing confirms strong internal consistency across TAM constructs. From a technological perspective, the system achieves TRL 6, indicating successful demonstration in a relevant operational environment. The findings suggest that a mobile-based smart inventory system offers a scalable, cost-effective alternative to hardware-dependent smart refrigerator solutions. This research contributes to sustainable consumption practices and provides a practical framework for digital interventions targeting household food waste reduction.Downloads
References
[1] Food and Agriculture Organization of the United and (FAO), The future of food and agriculture: trends and challenges, vol. 4, no. 4. 2014. [Online]. Available: www.fao.org/publications%0Ahttp://www.fao.org/3/a-i6583e.pdf%0Ahttp://siteresources.worldbank.org/INTARD/825826-1111044795683/20424536/Ag_ed_Africa.pdf%0Awww.fao.org/cfs%0Ahttp://www.jstor.org/stable/4356839%0Ahttps://ediss.uni-goettingen.de/bitstream/han
[2] M. Vahdanjoo, C. G. Sørensen, and M. Nørremark, “Digital transformation of the agri-food system,” Curr. Opin. Food Sci., vol. 63, p. 101287, 2025, doi: 10.1016/j.cofs.2025.101287.
[3] F. Faturrahman, E. S. Subhan, and S. Shoalihin, “Pengembangan UMKM Berbasis Transformasi Digital Dalam Mendorong Pertumbuhan Ekonomi Lokal,” Advances in Management & Financial Reporting, vol. 3, no. 3, pp. 990–1008, 2025, doi: 10.60079/amfr.v3i3.622.
[4] S.-P. Ma, M.-J. Hsu, H.-J. Chen, and C.-J. Lin, “RESTful API Analysis, Recommendation, and Client Code Retrieval,” Electronics (Basel)., vol. 12, no. 1252, 2023, doi: https://doi.org/10.3390/electronics12051252.
[5] S. Purnama, M. Kamal, and A. B. Yadila, “ApplicationofRESTfulMethodwithJWTSecurity andHaversineAlgorithmonWebService-Based TeacherAttendanceSystem,” International Transactions on Artificial Intelligence (ITALIC), vol. 2, no. 1, pp. 33–39, 2023.
[6] X. Zhang, G. Feng, and X. Sun, “Advanced technologies of soil moisture monitoring in precision agriculture: A Review,” J. Agric. Food Res., vol. 18, no. May, p. 101473, 2024, doi: 10.1016/j.jafr.2024.101473.
[7] M. J. Barons, L. E. Walsh, E. E. Salakpi, and L. Nichols, “A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks,” Agriculture (Switzerland), vol. 14, no. 3, 2024, doi: 10.3390/agriculture14030458.
[8] B. Marii and I. Zholubak, “Features of Development and Analysis of Rest Systems,” Advances in Cyber-Physical Systems, vol. 7, no. 2, 2022.
[9] S. Di Meglio, L. L. L. Starace, and S. Di Martino, “Starting a new REST API project? A performance benchmark of frameworks and execution environments,” CEUR Workshop Proc., vol. 3543, 2023.
[10] D. Felicio, J. Simao, and N. Datia, “Rapitest: Continuous black-box testing of restful web apis,” Procedia Comput. Sci., vol. 219, no. 2022, pp. 537–545, 2023, doi: 10.1016/j.procs.2023.01.322.
[11] H. Zhang, Strategic Analysis of Content Production for Broadcast Programs in a Multi-Platform Environment, no. Icedbc. Atlantis Press International BV, 2024. doi: 10.2991/978-94-6463-538-6_26.
[12] H. F. Herdiyatmoko, “Back-End System Design Based on Rest Api,” Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 5, no. 1, p. 123, 2022, doi: 10.37600/tekinkom.v5i1.401.
[13] A. S. Shethiya, “Academia Nexus Journal Building Scalable and Secure Web Applications Using .NET and Microservices,” Academia Nexus Journal, vol. 4, no. 1, pp. 1–7, 2025, [Online]. Available: https://academianexusjournal.com
[14] M. Kim, T. Stennett, D. Shah, S. Sinha, and A. Orso, “Leveraging Large Language Models to Improve REST API Testing,” Proceedings - International Conference on Software Engineering, no. February, pp. 37–41, 2024, doi: 10.1145/3639476.3639769.
[15] T. Nasution, Herwin, and K. Andesa, “RFID and Finger Print Based Dual Security System: A Robust Secured Control to Access Through Door Lock Operation,” American Journal of Embedded Systems and Applications, vol. 6, no. 1, p. 15, 2018, doi: 10.11648/j.ajesa.20180601.13.
[16] T. Dehling, Y. Zhang, and A. Sunyaev, “Consumer Perceptions of Online Behavioral Advertising,” 2019 IEEE 21st Conference …, 2019, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8808011/
[17] M. Kumar, “Designing Resilient Front End Architectures for Real-Time Web Application,” International Journal of Engineering Technology Research & Management, no. July, pp. 229–240, 2025.
[18] I. R. D. Muhammad and I. V. Paputungan, “Development of Backend Server Based on REST API Architecture in E-Wallet Transfer System,” Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi, vol. 3, no. 2, pp. 79–87, 2024, doi: 10.20885/snati.v3.i2.35.
[19] P. Gowda and A. N. Gowda, “Best Practices in REST API Design for Enhanced Scalability and Security,” Journal of Artificial Intelligence, Machine Learning and Data Science, vol. 2, no. 1, pp. 827–830, 2024, doi: 10.51219/jaimld/priyanka-gowda/202.
[20] C. S. Lepird, L. H. X. Ng, A. Wu, and K. M. Carley, “What News Is Shared Where and How: A Multi-Platform Analysis of News Shared During the 2022 U.S. Midterm Elections,” Social Media and Society, vol. 10, no. 2, 2024, doi: 10.1177/20563051241245950.
