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All Journal Building of Informatics, Technology and Science
Ardina Ariani
Universitas Sriwijaya, Palembang
Computer Science & IT

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Comparative Customer Segmentation Pipelines for E-Commerce Using K-Means-KNN and UMAP-K-Means-XGBoost Dzidan Aditya Gumilang; Endang Lestari Ruskan; Ardina Ariani; Ken Dhita Tania; Ahmad Rifai
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10794

Abstract

The rapid expansion of e-commerce has generated massive volumes of customer data that remain underutilized for supporting Customer Relationship Management (CRM) strategies. Conventional customer segmentation approaches commonly employ a pipeline consisting of K-Means clustering followed by K-Nearest Neighbors (KNN) classification. However, this approach exhibits limitations in handling high-dimensional data and maintaining classification performance on large-scale datasets. This study presents a comparative analysis of two customer segmentation pipelines: the conventional K-Means-KNN pipeline and the proposed Uniform Manifold Approximation and Projection (UMAP)-K-Means-XGBoost pipeline. The experiments were conducted using the E-Commerce Shopper Behavior & Lifestyle dataset, comprising approximately one million customer records and eight selected features representing transactional, psychographic, and financial behavioral characteristics. Clustering performance was evaluated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, while classification performance was assessed using accuracy, precision, recall, and F1-score. Experimental results demonstrate that incorporating UMAP improves cluster separability by preserving the intrinsic structure of high dimensional data, whereas XGBoost consistently outperforms KNN in downstream classification, achieving an accuracy exceeding 99%. These findings indicate that the UMAP-K-Means-XGBoost pipeline provides a more robust, scalable, and interpretable framework for customer segmentation, thereby offering more reliable decision support for data-driven CRM strategies in e-commerce environments.
Analisis Perbandingan Random Forest dan XGBoost dalam Prediksi Pembatalan Pesanan Shopee Menggunakan Interpretasi SHAP Muhammad Bayu Samudra; Mira Afrina; Allsela Meiriza; Rizka Dhini Kurnia; Ardina Ariani
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10960

Abstract

Order cancellation is a significant issue for e-commerce platforms because it can result in revenue loss, increased operational costs, and reduced transaction management efficiency. This study aims to compare the performance of Random Forest and Extreme Gradient Boosting (XGBoost) in predicting customer order cancellations on Shopee and to interpret the factors contributing to the prediction outcomes using an Explainable Artificial Intelligence approach based on SHapley Additive exPlanations (SHAP). The study employed the Shopee Consumer Behaviour Cancellation Order Analysis dataset obtained from Kaggle. The research process consisted of data preprocessing, dataset partitioning using the Stratified Train-Test Split method with an 80:20 ratio, implementation of both algorithms, model evaluation using Accuracy, Precision, Recall, F1-Score, and Receiver Operating Characteristic–Area Under Curve (ROC-AUC), followed by model interpretation using SHAP. The evaluation results indicate that Random Forest achieved better performance across most evaluation metrics, while XGBoost obtained a slightly higher ROC-AUC value. SHAP analysis identified Total Discount, Buyer-Paid Shipping Cost, Estimated Shipping Cost, and Estimated Shipping Fee Deduction as the variables contributing most to order cancellation predictions.These findings indicate that combining predictive algorithms with an Explainable Artificial Intelligence approach not only supports the classification of order cancellations but also provides insights into the factors influencing prediction outcomes, which can serve as a consideration for reducing order cancellations on e-commerce platforms.
Co-Authors Ahmad Rifai Allsela Meiriza Dzidan Aditya Gumilang Endang Lestari Ruskan Ken Dhita Tania Mira Afrina Muhammad Bayu Samudra Rizka Dhini Kurnia