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All Journal JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Sidharta, Robertus
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Computer Science & IT Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering

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Hybrid Clustering with Deep Learning in E-commerce for Customer Segmentation: A Data-Driven Approach for Business Strategy Optimization Sidharta, Robertus; Riyadi, Agung; Hanfiro, Pauline; Handini, Mia
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): February
Publisher : Lumina Infinity Academy Foundation

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Abstract

Customer segmentation is a strategic approach to understanding customer needs and preferences, especially in the dynamic e-commerce industry. Traditional clustering methods, such as k-means, are often used for this task, but have limitations in handling complex and high-dimensional data. In this research, we use a hybrid clustering approach that integrates deep learning for feature extraction with traditional clustering algorithms for customer segmentation. Uses Mall Customers Dataset from Kaggle, which includes customer demographic and shopping behavior data. Experimental results show that this approach is able to produce more accurate and meaningful segmentation. The visualization of the results shows significant patterns that can be used to develop more personalized and effective marketing strategies.
Co-Authors Agung Riyadi Handini, Mia Hanfiro, Pauline