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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Fauziyah Nurul Isnaini
Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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LEVERAGING XGBOOST, LIGHTGBM, AND CATBOOST FOR ENHANCED CUSTOMER SEGMENTATION IN THE AUTOMOTIVE INDUSTRY Novri Suhermi; Rahida Rihhadatul Aisy; Aulia Afifatur Rohmah; Anis Alif Nurhayati; Agnes Nathania Pramesty; Aura Lovi Ardanika; Fauziyah Nurul Isnaini
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2281-2298

Abstract

This study evaluates the performance of three gradient boosting algorithms, XGBoost, LightGBM, and CatBoost, for customer segmentation in the automotive industry. Utilizing a dataset of 8,068 training and 2,627 testing observations with 11 demographic and behavioral variables, the research aims to classify customers into four segments. The methodology includes preprocessing (handling missing values, encoding), hyperparameter tuning via Randomized Search Cross-Validation, and evaluation using ROC AUC. Results indicate that XGBoost outperforms other models, achieving an AUC of 0.5837 on testing data with significant variables, while LightGBM and CatBoost scored 0.5834 and 0.5759, respectively. Key findings highlight the importance of feature selection, with Age, Profession, and Spending Score being the most influential predictors. The study concludes that XGBoost is the most robust for segmentation tasks, though all models exhibit challenges in distinguishing overlapping classes. These insights can guide data-driven marketing strategies in automotive and related sectors.
Co-Authors Agnes Nathania Pramesty Anis Alif Nurhayati Aulia Afifatur Rohmah Aura Lovi Ardanika Novri Suhermi Rahida Rihhadatul Aisy