Penelitian ini akan merancang proyek digital terkait sistem rekomendasi makanan dan minuman (F&B) berbasis preferensi pengguna yang dirancang untuk memberikan solusi dalam kebimbangan memilih konsumsi harian. Pengembangan melibatkan tahapan proyek digital, mulai dari perencanaan, analisis kebutuhan, analisis teknis, desain proses, dan penerapan mockup antarmuka yang terintegrasi dengan aplikasi delivery pihak ketiga. Hasil sistem ini akan menjadi landasan bagi perancangan antarmuka aplikasi yang user-friendly dan sesuai dengan kebutuhan pengguna. Proyek ini akan mengembangkan aplikasi yang memanfaatkan teknologi analisis big data dan kecerdasan buatan untuk menyediakan rekomendasi personal secara real-time. Aplikasi ini bertindak sebagai add-on yang terintegrasi dengan layanan pemesanan online, mempermudah pengguna dalam memilih makanan atau minuman yang sesuai preferensi. Dengan fitur personalisasi, pengguna dapat menentukan preferensi makanan, masakan favorit, dan batasan diet. Sistem rekomendasi memanfaatkan data pengguna, aktivitas, dan filter kualitas seperti rating merchant, untuk meningkatkan relevansi hasil.

Dengan merancang sistem rekomendasi makanan dan minuman (F&B) berbasis preferensi pengguna , penelitian ini bertujuan tidak hanya untuk mendukung kebutuhan konsumen, tetapi juga membantu industri F&B dalam meningkatkan jangkauan dan penjualan melalui rekomendasi berbasis data. Implementasi aplikasi ini diharapkan memberikan manfaat signifikan bagi pengguna individu, industri makanan, dan pengembangan teknologi rekomendasi di Indonesia.

This research will design a digital project related to a food and beverage (F&B) recommendation system based on user preferences, designed to provide solutions for the dilemma of choosing daily consumption. The development involves digital project stages, starting from planning, needs analysis, technical analysis, process design, and the implementation of a mockup interface integrated with third-party delivery applications. The results of this system will be the foundation for designing a user-friendly application interface that meets user needs. This project will develop an application that utilizes big data analytics and artificial intelligence technology to provide real-time personalized recommendations. The application acts as an add-on integrated with online ordering services, making it easier for users to choose food or beverages that suit their preferences. With personalization features, users can specify food preferences, favorite cuisines, and dietary restrictions. The recommendation system utilizes user data, activities, and quality filters such as merchant ratings to improve the relevance of results. By designing a food and beverage (F&B) recommendation system based on user preferences, this research aims not only to support consumer needs but also to help the F&B industry in increasing reach and sales through data-driven recommendations. The implementation of this application is expected to provide significant benefits for individual users, the food industry, and the development of recommendation technology in Indonesia.

Christiane, F., Tanria, D., & Sutrisno, J.. (2024). Perancangan Inventory System untuk Mengoptimalkan Operasional Penjualan, Pembelian, dan Pengembalian pada CV.Victory. Reslaj: Religion Education Social Laa Roiba Journal, 6(4), 2545 –. https://doi.org/10.47467/reslaj.v6i4.1408

de Cheveigné, A., & Nelken, I. (2019). Filters: When, Why, and How (Not) to Use Them. Neuron, 102(2), 280–293. https://doi.org/10.1016/j.neuron.2019.02.039

Edward, E., Sugianto, M., & Sutrisno, J. (2024). Increasing Efficiency and Effectiveness through Digitalization in Batik Cooperative Business Processes (Kombas). Jurnal Indonesia Sosial Teknologi, 5(6), 2980

Guo, Q., Zhuang, F., Qin, C., Zhu, H., Xie, X., Xiong, H., & He, Q. (2023). A Survey on Knowledge Graph-Based Recommender Systems : Extended Abstract. Proceedings – International Conference on Data Engineering, 2023-April, 3803–3804. https://doi.org/10.1109/ICDE55515.2023.00328

Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019- 0206-3

Khan, Z. F., & Alotaibi, S. R. (2020). Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective. Journal of Healthcare Engineering, 2020. https://doi.org/10.1155/2020/8894694

Kolajo, T., Daramola, O., & Adebiyi, A. (2019). Big data stream analysis: a systematic literature review. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0210-7

Konttinen, H. (2020). Emotional eating and obesity in adults: The role of depression, sleep and genes. Proceedings of the Nutrition Society, 79(3), 283–289. https://doi.org/10.1017/S0029665120000166

Li, C., Mirosa, M., & Bremer, P. (2020). Review of online food delivery platforms and their impacts on sustainability. Sustainability (Switzerland), 12(14), 1–17. https://doi.org/10.3390/su12145528

McLeod, S. (2014). Maslow’s Hierarchy of Needs. A Theory of Human Motivation. Psychological. Psychological Review, 7(38), 2014. http://kalyan-city.blogspot.com/2010/06/maslow-hierarchyof-needs-theory-of.html

Nikzad-Khasmakhi, N., Balafar, M. A., Reza Feizi-Derakhshi, M., & Motamed, C. (2021). BERTERS: Multimodal representation learning for expert recommendation system with transformers and graph embeddings. Chaos, Solitons and Fractals, 151. https://doi.org/10.1016/j.chaos.2021.111260

Sun, A. Y., & Scanlon, B. R. (2019). How can Big Data and machine learning benefit environment and water management: A survey of methods, applications, and future directions. Environmental Research Letters, 14(7). https://doi.org/10.1088/1748-9326/ab1b7d

Tonchia, S. (2018). Project Quality Management. Management for Professionals, Part F482(January), 93–113. https://doi.org/10.1007/978-3-662-56328-1_9

Truong, N. B., Sun, K., Lee, G. M., & Guo, Y. (2020). GDPR-Compliant Personal Data Management: A Blockchain-Based Solution. IEEE Transactions on Information Forensics and Security, 15, 1746–1761. https://doi.org/10.1109/TIFS.2019.2948287

Wu, S., Sun, F., Zhang, W., Xie, X., & Cui, B. (2022). Graph Neural Networks in Recommender Systems: A Survey. ACM Computing Surveys, 55(5). https://doi.org/10.1145/3535101

Yin, C., Ding, S., & Wang, J. (2019). Mobile marketing recommendation method based on user location feedback. Human-Centric Computing and Information Sciences, 9(1). https://doi.org/10.1186/s13673-019-0177-6

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, 52(1). https://doi.org/10.1145/3285029