Product Recommendation System Using User-Based Collaborative Screening Methods In Digital Marketing

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Artikel (Bahasa Indonesia)
Published Jul 13, 2023
DOI : https://doi.org/10.32664/j-intech.v11i1.866
  • Halaman : 158 - 166
  • Abstract views : 121
  • PDF views : 154

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Satia Suhada
Program Studi Sistem Informasi, Universitas Bina Sarana Informatika, Sukabumi, Indonesia
Saeful Bahri
Program Studi Ilmu Komputer, Universitas Bina Sarana Informatika, Sukabumi, Indonesia
Setyo Bagus Nugraha
Program Studi Ilmu Komputer, Universitas Bina Sarana Informatika, Sukabumi, Indonesia
Taufik Hidayatulloh
Program Studi Sistem Informasi, Universitas Bina Sarana Informatika, Sukabumi, Indonesia
Dede Wintana
Universitas Bina Sarana Informatika

Abstract

The recommendation system has been implemented in digital marketing used in marketing products and services. The recommendation system is used to provide offers of goods and services in accordance with customer habits and interests in the proposed products and services, but in practice the right product offering for customers leads to the idea of developing a product recommendation system. Purchase data obtained from customers can be used to analyze customer needs and product preferences. In the recommendation system, Collaborative Filtering is one of the most commonly used algorithms. The purpose of this study is to find out how accurate the recommendation system is based on the purchase of similar goods between consumers using User-based Collaborative Filtering. Based on the results of the study, User-based Collaborative Filtering using Cosine Similarity calculations can be applied and produce 10 product recommendations with an RMSE value of 0.9.

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Issue
Vol 11 No 1 (2023): J-Intech : Journal of Information and Technology
Section
Articles
Copyright (c) 2023 J-INTECH (Journal of Information and Technology)
References
Alhijawi, B., & Kilani, Y. (2020). A collaborative filtering recommender system using genetic algorithm. Information Processing and Management, 57(6). https://doi.org/10.1016/j.ipm.2020.102310
Badan Pusat Statistik. (n.d.). Statistik E-Commerce.
Bagaskara, R., Khairunnisa Rizkita, A., Fernandes, R., Yulita, W., Studi, P., Informatika, T., Produksi, J. T., Industri, D., Sumatera, T., Ryacudu, J. T., Huwi, W., Agung, K. J., Kabupaten, L., & Selatan, L. (2022). Pendeteksian Jumlah Bangunan Berbasis Citra Menggunakan Metode Deep Learning. Jurnal Sains Komputer & Informatika (J-SAKTI, 6(1), 94–100.
Feng, J., Xia, Z., Feng, X., & Peng, J. (2021). RBPR: A hybrid model for the new user cold start problem in recommender systems. Knowledge-Based Systems, 214. https://doi.org/10.1016/j.knosys.2020.106732
Khusna, A. N., Delasano, K. P., & Saputra, D. C. E. (2021). Penerapan User-Based Collaborative Filtering Algorithm. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 20(2), 293–304. https://doi.org/10.30812/matrik.v20i2.1124
Putra, R. P., Subari, S., & Aminah, S. (2022). Aplikasi Pendukung Keputusan Perbaikan Serta Penyedia Jasa Service Komputer Dan Laptop Menggunakan Metode Naïve Bayes. J-Intech, 9(02), 79–90. https://doi.org/10.32664/j-intech.v9i02.636
Rahmadi, T. (2020). The Power Of Digital Marketing.
Sutawinaya, I. P., Astawa, I. N. G. A., & Hariyanti, N. K. D. (2017). Perbandingan Metode Jaringan Saraf Tiruan Pada Peramalan Curah Hujan. Prosiding SNSebatik 2017.
Wahyuni Windasari, T. Z. (2021). Pemetaan dan Analisis Faktor yang Mempengaruhi Persentase Usaha E-Commerce di Indonesia. Akuntasi, Ekonomi Dan Manajemen Bisnis, 9(2), 130–135.