Aplikasi Sistem Rekomendasi F&B Sesuai Preferensi Pengguna
Abstract
Kebimbangan dalam memilih makanan dan minuman (F&B) yang sesuai dengan preferensi dan kondisi emosional menjadi masalah bagi banyak pengguna, terutama di tengah banyaknya pilihan yang tersedia. Pengaruh emosi dan kebutuhan akan rekomendasi yang tepat semakin menambah kompleksitas pengambilan keputusan. Penelitian ini bertujuan mengembangkan aplikasi sistem rekomendasi F&B berbasis mobile yang memberikan rekomendasi personal secara real-time, membantu pengguna dalam memilih konsumsi yang sesuai. Dengan memanfaatkan analisis big data, aplikasi ini dapat memproses data pengguna secara efisien dan memberikan rekomendasi yang akurat dan relevan. Kebaruan penelitian pada integrasi sistem rekomendasi menggunakan metode MADLC dengan platform pemesanan online, yang memungkinkan pengguna mendapatkan rekomendasi yang dipersonalisasi sesuai preferensi mereka dan langsung dapat mengambil keputusan secara mudah. Meskipun demikian, tantangan tetap ada, seperti pengumpulan data personal yang akurat dan pengujian aplikasi yang masih terbatas pada kelompok tertentu. Urgensi penelitian ini semakin meningkat dengan tren layanan digital dan pemesanan makanan yang kian populer di era modern.
References
Andry, J. F., Bernanda, D. Y., Lee, F. S., Christianto, K., & Setiawan, S. (2024). Komparasi harga terhadap harga aplikasi ojek online. Infotech: Jurnal of Techology Information, 10(2), 353–360. https://doi.org/10.37365/jti.v10i2.329
Geasela, Y. M., Lee, F. S., Bernanda, D. Y., Agustina, A., & Andrian, A. (2021). Pemanfaatan Aplikasi Pengolah Presentasi Dalam Mendukung Pembelajaran Di Sma Tarsisius 1 Jakarta. Jurnal Pengabdian Dan Kewirausahaan, 5(1), 28–35. https://doi.org/10.30813/jpk.v5i1.2723
Grantz, K. H., Meredith, H. R., Cummings, D. A. T., Metcalf, C. J. E., Grenfell, B. T., Giles, J. R., Mehta, S., Solomon, S., Labrique, A., Kishore, N., Buckee, C. O., & Wesolowski, A. (2020). The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nature Communications, 11(1), 1–8. https://doi.org/10.1038/s41467-020-18190-5
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
Kaffash, S., Nguyen, A. T., & Zhu, J. (2021). Big Data Algorithms And Applications In Intelligent Transportation System: A Review And Bibliometric Analysis. International Journal of Production Economics, 231(December 2019), 107868. https://doi.org/10.1016/j.ijpe.2020.107868
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, 1–15. 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
Lee, F. S., & Isputrawan, M. F. (2022). Peningkatan Kualitas Layanan Warga Kelurahan Duri Kepa dengan Aplikasi LINGKOE. Jurnal Informatika, 9(1), 61–70. https://doi.org/10.31294/inf.v9i1.11538
Lee, F. S., Nurprihatin, F., Santoso, A. P., & Tampinongkol, F. F. (2024). Aplikasi Pelaporan Kerja Cleaning Service dengan Metode Waterfall. Infotech: Jurnal of Techology Information, 10(1), 61–70. https://doi.org/10.37365/jti.v10i1.248
McLeod, S. (2014). Maslow’s Hierarchy of Needs. A Theory of Human Motivation. Psychological. Psychological Review, 7(38), 2014.
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
Ramdhani, Y., Prasetio, R. T., & Nugroho, T. W. (2021). Decision Support System of Food and Beverage Franchise Business. Proceedings of the International Conference on Industrial Engineering and Operations Management Monterrey, Nov(3–5), 3120–3130. http://ieomsociety.org/proceedings/2021monterrey/542.pdf
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
Thongsri, N., Warintarawej, P., Chotkaew, S., & Saetang, W. (2022). Implementation of a personalized food recommendation system based on collaborative filtering and knapsack method. International Journal of Electrical and Computer Engineering, 12(1), 630–638. https://doi.org/10.11591/ijece.v12i1.pp630-638
Tian, Y., Zheng, B., Wang, Y., Zhang, Y., & Wu, Q. (2019). College library personalized recommendation system based on hybrid recommendation algorithm. Procedia CIRP, 83(March), 490–494. https://doi.org/10.1016/j.procir.2019.04.126
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
Yuan, Z., & Chen, Y. (2024). How Recommendation Affects Customer Search : A Field Experiment. Informs Pubs Online. https://doi.org/10.1287/isre.2022.0294
Zhang, F., Liu, Y., Mao, J., Zhang, M., & Ma, S. (2020). User Behavior Modeling for Web Search Evaluation. AI Open, 1(March), 40–56. https://doi.org/10.1016/j.aiopen.2021.02.003
Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex and Intelligent Systems, 7(1), 439–457. https://doi.org/10.1007/s40747-020-00212-w
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
Copyright (c) 2025 Francka Sakti Lee, Putu Sita Witari, Johanes Fernandes Andry, Honni Honni
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
