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All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Jurnal Ilmiah Mahasiswa Jurnal Kridatama Sains dan Teknologi JTIK (Jurnal Teknik Informatika Kaputama) SOLUSI Jurnal Disastri: Pendidikan Bahasa dan Sastra Indonesia Dedikasi: Jurnal Pengabdian Kepada Masyarakat BERBAKTI: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Multidisiplin Ilmu COSMOS Jejak digital: Jurnal Ilmiah Multidisiplin
Ana Fauziah
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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PENDEKATAN ALGORITMA TREE-BASED DAN SHAP UNTUK ANALISIS FAKTOR CHURN PELANGGAN E-COMMERCE Fauziah, Ana; Prasanti Mia Purnama
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 10 No. 2 (2026): Artificial Intelligence (AI)
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v10i2.1307

Abstract

The rapid growth of the e-commerce industry has intensified competition and increased the risk of customer churn, defined as the discontinuation of service usage. This study aims to develop an accurate churn prediction model using four tree-based classifiers—Random Forest, XGBoost, LightGBM, and CatBoost—and to evaluate feature contributions using SHAP (Shapley Additive Explanations). The study employs the E-commerce Customer Churn dataset from Kaggle, consisting of 5,630 observations and 20 behavioral features. All models demonstrate strong performance, achieving accuracy above 95%. XGBoost outperforms the other models, with an accuracy of 97.42% and an F1-score of 0.918, indicating a good balance between precision and recall. SHAP analysis identifies Tenure as the most influential feature, followed by Complain, NumberOfAddress, CashbackAmount, and MaritalStatus_Married. Lower tenure increases churn likelihood, while higher complaint levels significantly raise churn risk. Feature effects vary across individuals due to interactions among variables. These findings highlight the importance of interpretability in supporting accurate and data-driven customer retention strategies in e-commerce.
Co-Authors Ahya, Akhmad Sauqi Budiyono Budiyono Damayanti, Elok Hermanto Hermanto Mursid Raharjo Natasya Ingga Felani Fauzi Nugroho, Dwi Widi Pratito Sri Prasanti Mia Purnama Sofiarini, Andriana Sukmarini, Mita Akbar Sulistyowati, Heny Wardana, Edy Surya Yolanda, Yaspin