E-Commerce Recommender System on the Shopee Platform Using Apriori Algorithm

Authors

  • Rachmi Helfianur Telkom University
  • ZK Abdurahman Baizal Computational Science, Faculty of Informatics, Telkom University

DOI:

https://doi.org/10.34818/INDOJC.2022.7.2.650

Keywords:

Recomennder System, Apriori Algorithm, E-Commerce, Association Rules

Abstract

The development of E-Commerce continues to increase every year, and all online shopping platforms continue to increase competition. Shopee as an online shopping platform offers various product categories that users need. To make it easier for users when shopping online, it is necessary to implement a product recommender system in E-Commerce. Therefore, in this study, we will build a recommender system using the a priori algorithm. The apriori algorithm is very widely used to find out the buying pattern of each user by looking at a combination of itemset. many recommender systems in e-commerce use various methods used, and provide recommendation results that display popular products, and based on the query results obtained. From the results of previous studies, there are similarities between products that have been liked by customers, so they do not have the best recommendations. Therefore, in this study we apply an apriori algorithm to add user confidence to the given recommendations, and to avoid overspecialization. In this research, we take the domain of electronics goods. In this study, the system produces the best value for association rules with a support value of 0.01, confidence 1.00, and lift 97.35.

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References

Ching, K. C., Hasan, Z. R., & Hasan, N. A. (2021). Factors influencing consumers in using shopee for online purchase intention in east coast Malaysia. UMT Journal of Undergraduate Research, 3(1), 45-56.

Baizal, Z. A., Widyantoro, D. H., & Maulidevi, N. U. (2016, October). Query refinement in recommender system based on product functional requirements. In 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 309-314). IEEE.

Baizal, Z. K. A., Widyantoro, D. H., & Maulidevi, N. U. (2020). Computational model for generating interactions in conversational recommender system based on product functional requirements. Data & Knowledge Engineering, 128, 101813.

Chandra, A., Ahmed, A., Kumar, S., Chand, P., Borah, M. D., & Hussain, Z. (2022). Content-Based Recommender System for Similar Products in E-Commerce. In Edge Analytics (pp. 617-628). Springer, Singapore.

Koesmyta, R. P. (2020). Pembuatan E-Commerce Berbasis Web Dengan Sistem Rekomendasi Content-Based Filtering Menggunakan Algoritma Apriori (Doctoral dissertation, Universitas 17 Agustus 1945 Surabaya).

Badriyah, T., Fernando, R., & Syarif, I. (2018). Sistem Rekomendasi Content Based Filtering Menggunakan Algoritma Apriori. Konferensi Nasional Sistem Informasi (KNSI) 2018.

Riyadi, N., Mulki, M. F., & Susanto, R. (2019). Analysis of Customers Purchase Patterns of E-Commerce Transactions Using Apriori Algorithm and Sales Forecasting Analysis With Weighted Moving Average (WMA) Method. Scientific Research Journal.

WANG, X. Y. (2017). An Apriori-Based E-Commerce Recommendation System. DEStech Transactions on Economics, Business and Management, (icem).

Pradana, H. A., Laurentinus, F. P. J., & Sylfania, D. Y. (2020). Product Recommendation Systems using Apriori in the Selection of Shoe based on Android.

Vishwakarma, G., Parte, S., Joshi, H., Patel, J., & Jangid, P. (2019). Online Sequential Behaviour Analysis using Apriori Algorithm.

Fatoni, C. S., Utami, E., & Wibowo, F. W. (2018, December). Online Store Product Recommendation System Uses Apriori Method. In Journal of Physics: Conference Series (Vol. 1140, No. 1, p. 012034). IOP Publishing.

J. Han, M. Kamber and J. Pei, 2012, Data Mining: Concepts and Techniques Third Edition, Elsevier

Kusrini, Luthfi, E. T., 2009, Algoritma Data Mining, Andi Offset, Yogyakarta

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Published

2022-08-01

How to Cite

Helfianur, R. ., & Baizal, Z. A. (2022). E-Commerce Recommender System on the Shopee Platform Using Apriori Algorithm. Indonesian Journal on Computing (Indo-JC), 7(2), 53–64. https://doi.org/10.34818/INDOJC.2022.7.2.650

Issue

Vol. 7 No. 2 (2022): August, 2022

Section

Computer Science

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