PENERAPAN COLLABORATIVE FILTERING PADA SISTEM PEMESANAN MENU KAFE UNTUK MENINGKATKAN CROSS-SELLING BERBASIS ANDROID
DOI:
https://doi.org/10.51401/jinteks.v6i4.4874Keywords:
Collaborative Filtering, Android, Cross-Selling, PemesananAbstract
Cafes have become an essential part of lifestyle in today's digital era, with increasing customer demand for unique and diverse culinary experiences. This study aims to develop an Android-based cafe menu ordering system by implementing the Collaborative Filtering method to improve cross-selling practices. This system is designed to provide personalized menu recommendations based on customer preferences and behavior. Using previous transaction data, this system can suggest additional products that are relevant to the customer's main order, such as suggesting cakes or snacks with coffee. The implementation of this technology is expected to increase customer satisfaction and transaction value in cafes.
References
M. Rahmawita and A. Wiratama, “Aplikasi Pemesanan Menu Makanan Restoran dan Cafe Berbasis Android,” Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, vol. 7, no. 1, pp. 76–82, 2021.
A. Sunandar and R. Lubis, “PENERAPAN CROSS SELLING PADA SISTEM CUSTOMER RELATIONSHIP MANAGEMENT PENJUALAN BUKU,” Jurnal Ilmiah Komputer dan Informatika (KOMPUTA), vol. 7, no. 2, 2018.
F. Nurhani and Samsudin, “Implementasi Algoritma Collaborative Filtering pada Sistem Pemesanan Makanan dan Minuman dengan Platform Android,” Jurnal Ilmiah Komputasi, vol. 21, no. 3, pp. 317–332, Sep. 2022.
B. Prasetyo, H. Haryanto, S. Astuti, E. Z. Astuti, and Y. Rahayu, “Implementasi Metode Item-Based Collaborative Filtering dalam Pemberian Rekomendasi Calon Pembeli Aksesoris Smartphone,” Eksplora Informatika, vol. 9, no. 1, pp. 17–27, Sep. 2019, doi: 10.30864/eksplora.v9i1.244.
S. Bahri et al., “Implementasi Sistem Rekomendasi Makanan pada Aplikasi EatAja Menggunakan Algoritma Collaborative Filtering,” MULTINETICS, vol. 7, no. 2, pp. 177–185, Mar. 2022.
E. Jayadi, B. Mulyawan, and M. D. Lauro, “Implementasi Metode Collaborative Filtering Untuk Analisis Data Belanja Konsumen Berbasis Website (Studi Kasus Restoran Mykitchen),” Jurnal Ilmu Komputer dan Sistem Informasi, vol. 9, no. 1, pp. 57–61, 2021.
H. A. Tambunan and J. H. P. Sitorus, “Sistem Rekomendasi Collaborative Filtering Sebagai Upaya Peningkatan Perekonomian di Pasar Tradisional,” 2023.
S. N. Mohanty, J. M. Chatterjee, S. Jain, A. A. Elngar, and P. Gupta, Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries. Wiley, 2020.
S. Diamond, Digital Marketing All-in-One For Dummies. New York: Wiley, 2019.
I. Rahmawati and D. P. Sari, “APLIKASI BERBASIS ANDROID MENGGUNAKAN FLUTTER FRAMEWORK UNTUK KEPERLUAN PERIZINAN TUGAS KELUAR PADA PT. XYZ,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 2, pp. 979–993, May 2024, doi: 10.29100/jipi.v9i2.5489.
A. Kartini and S. Hidayatulloh, “Aplikasi Sistem Pemesanan Menu Pada Kafe Nami Kopiminasi Dengan Menggunakan Metode Waterfall,” Jurnal Infortech, vol. 5, no. 2, pp. 123–132, Dec. 2023, doi: 10.31294/infortech.v5i2.17238.
R. Destriana, S. M. Husain, N. Handayani, and A. T. P. Siswanto, Diagram UML Dalam Membuat Aplikasi Android Firebase “Studi Kasus Aplikasi Bank Sampah.” Deepublish, 2021.
S. Bagui and R. Earp, Database Design Using Entity-Relationship Diagrams. Florida: CRC Press, 2022.
M. R. Maulana, B. Susanto, and A. Christianto, “KLIK: Kajian Ilmiah Informatika dan Komputer Pengujian Black Box dengan Teknik Equivalence Partitioning pada Aplikasi Monitoring Pemberian Obat Filariasis Berbasis Android,” Media Online, vol. 4, no. 4, pp. 2179–2187, 2024, doi: 10.30865/klik.v4i4.1603.
Published
How to Cite
Issue
Section
Copyright (c) 2024 Rafly Maulana Daulah, Rr. Hajar Puji Sejati

This work is licensed under a Creative Commons Attribution 4.0 International License.
















