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All Journal Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF)
Kafilla, Princess Iqlima
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Computer Science & IT

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Enhancing Customer Purchase Behavior Prediction Using PSO-Tuned Ensemble Machine Learning Models Kafilla, Princess Iqlima; Utomo, Fandy Setyo; Karyono, Giat
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4952

Abstract

Predicting customer purchase behavior remains a significant challenge in e-commerce and marketing analytics due to its complex and nonlinear patterns. This study introduces a machine learning framework that integrates ensemble learning models with Particle Swarm Optimization (PSO) for hyperparameter tuning to improve classification accuracy and class discrimination. Several ensemble algorithms, including CatBoost, XGBoost, LightGBM, AdaBoost, and Gradient Boosting, were compared against a baseline Logistic Regression model, both with default and PSO-optimized configurations. Experiments on a real-world e-commerce dataset containing behavioral and demographic variables showed that ensemble methods substantially outperformed traditional models across accuracy, F1-score, and ROC AUC metrics. Notably, the PSO-tuned Gradient Boosting model achieved the highest ROC AUC of 0.9547, improving the AUC by approximately 0.0076 compared to its default configuration, while CatBoost obtained the highest overall accuracy and F1-score. PSO optimization was especially effective in enhancing simpler models such as Logistic Regression but showed marginal gains and some convergence instability in more complex ensemble models. Feature importance analyses consistently identified variables such as time spent on the website, discounts availed, age, and income as key drivers of purchase intent. These findings demonstrate the benefit of combining ensemble learning with metaheuristic optimization, offering actionable insights for developing robust, data-driven marketing strategies.
Studi Komparasi Kinerja Algoritma AdaBoost dan CatBoost dalam Prediksi Perilaku Pembelian Pelanggan Kafilla, Princess Iqlima; Utomo, Fandy Setyo; Karyono, Giat
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.7947

Abstract

Customer purchase behavior is a crucial factor in the development of effective marketing strategies. By leveraging predictive analytics, businesses can personalize recommendations, optimize marketing campaigns and improve user experience, ultimately contributing to increased conversion rates and customer retention. This research compares the performance of AdaBoost and CatBoost algorithms in predicting customer purchase behavior. The dataset used includes demographic attributes and customer behavior history, allowing for comprehensive analysis. The results showed that CatBoost performed better overall with an accuracy of 94%, while AdaBoost showed higher recall and F1-score values in the positive class. This study concludes that both algorithms have reliability in predicting customer behavior, where CatBoost is superior in handling categorical features, while AdaBoost offers good adaptability on simpler datasets. As a next step, future research can explore the implementation of these models in real-time scenarios.
Co-Authors Fandy Setyo Utomo Giat Karyono