Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm

Authors

  • Mohd Shareduwan Mohd Kasihmuddin School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • Nur Shahira Abdul Halim School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • Siti Zulaikha Mohd Jamaludin School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • Mohd. Asyraf Mansor School of Distance Education, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • Alyaa Alway School of Distance Education, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • Nur Ezlin Zamri School of Distance Education, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • Siti Aishah Azhar School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • Muhammad Fadhil Marsani School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia

DOI:

https://doi.org/10.32890/jict2023.22.3.1

Keywords:

2-satisfiability, genetic algorithm, Hopfield neural network, logic mining, online shopping

Abstract

Online shopping is a multi-billion-dollar industry worldwide. However, several challenges related to purchase intention can impact the sales of e-commerce. For example, e-commerce platforms are unable to identify which factors contribute to the high sales of a product. Besides, online sellers have difficulty finding products that align with customers’ preferences. Therefore, this work will utilize an artificial neural network to provide knowledge extraction for the online shopping industry or e-commerce platforms that might improve their sales and services. There are limited attempts to propose knowledge extraction with neural network models in the online shopping field, especially research revolving around online shoppers’ purchasing intentions. In this study, 2-satisfiability logic was used to represent the shopping attribute and a special recurrent artificial neural network named Hopfield neural network was employed. In reducing the learning complexity, a genetic algorithm was implemented to optimize the logical rule throughout the learning phase in performing a 2-satisfiability-based reverse analysis method, implemented during the learning phase as this method was compared. The performance of the genetic algorithm with 2-satisfiability-based reverse analysis was measured according to the selected performance evaluation metrics. The simulation suggested that the proposed model outperformed the existing model in doing logic mining for the online shoppers dataset. 

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Published

24-07-2023

Issue

Vol. 22 No. 3 (2023): Journal of Information and Communication Technology (JICT) Vol.22, No.3, July 2023

Section

Articles

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Copyright (c) 2023 Journal of Information and Communication Technology

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

How to Cite

Mohd Kasihmuddin, M. S., Abdul Halim, N. S., Mohd Jamaludin, S. Z., Mansor, M. A., Alway, A., Zamri, N. E., Azhar, S. A., & Marsani, M. F. (2023). Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm. Journal of Information and Communication Technology, 22(3), 309-335. https://doi.org/10.32890/jict2023.22.3.1

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Identifiers DOI 10.32890/jict2023.22.3.1 OpenAlex W4386103451 Scopus 85168587665