XGBOOST-BASED CLASSIFICATION OF POTENTIALLY FRAUDULENT SHOPEE SELLERS USING SELLER METADATA AND SHAP
DOI:
https://doi.org/10.34288/jri.v8i4.651Keywords:
Fraud Detection, Shopee, XGBoost, SHAP, Seller, Explainable AIAbstract
The rapid growth of e-commerce increases the exposure to fraudulent sellers, while reactive reporting mechanisms generally operate only after consumers suffer losses. This study develops a proactive fraud-risk screening model to assist in identifying potential fraud-risk indications among Shopee sellers from store-profile metadata and to explain the model's decisions. Primary data were collected from Indonesian Shopee seller profiles using a manual-hybrid approach with Playwright. The initial dataset contained 460 profiles and was reduced to 457 clean records after duplicate removal. The final dataset comprised 355 Safe stores and 102 Fraud stores (labeled heuristically using external indicators) with 11 predictor features. XGBoost was trained with class weighting through a scale_pos_weight value of 3.48 and an 80:20 stratified split. Evaluation on 92 test records produced 95.65% Accuracy, 84.00% Precision, 100.00% Recall, and 91.30% F1-Score. SHAP analysis identified historical_sold (48.42%), store age (18.73%), and rating_star (14.76%) as the three most influential features. The trained model was subsequently integrated into a Chrome Extension prototype to provide predictions and SHAP-based rationales. The results indicate that a seller-centric metadata approach can support early fraud-risk detection; however, the 84.00% precision confirms that the output should be interpreted as a risk screening indication rather than an absolute determination of fraud, assisting consumers in making safer decisions.
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References
Adams, V. (2024). Graph Neural Networks Analytics and AI for Fraud Detection. https://doi.org/10.64206/vvc03q65
Ardhitha, R., Anugerah, R., & Sutabri, T. (2025). Analisis Penerapan Machine Learning dan Algoritma Anomali untuk Deteksi Penipuan pada Transaksi Digital. Repeater: Publikasi Teknik Informatika dan Jaringan, 3(1), 80–90. https://doi.org/10.62951/repeater.v3i1.345
Awalia, S. A., Jedha Arhansyah, R., Rahadian Adi Nugroho, F., & Amzar Thapsuandji, A. (2024). Perlindungan Hukum terhadap Konsumen dalam Transaksi E-commerce Shopee. Jurnal Potensial, 3(1). https://jurnalilmiah.org/journal/index.php/potensial/article/view/740
Awosika, T., Shukla, R. M., & Pranggono, B. (2023). Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3394528
Bucci, V., & Li, W. (2023). From Manifest V2 to V3: A Study on the Discoverability of Chrome Extensions. Springer. https://doi.org/10.1007/978-3-031-49187-0_10
Chen, F. (2024). Research on Real-time E-commerce Price Comparison System Using Python Web Scraping Technology. International Journal of Computer Science and Information Technology, 4(2), 127–136. https://doi.org/10.62051/ijcsit.v4n2.18
Dekou, R., Savo, S., Kufeld, S., Francesca, D., & Kawase, R. (2022). Machine Learning Methods for Detecting Fraud in Online Marketplaces. arXiv preprint. https://doi.org/10.48550/arXiv.2205.00000
Farsi, S., & Chowdhury, M. (2025). EcomFraudEX: An Explainable Machine Learning Framework for Victim-Centric and Dual-Sided Fraud Incident Classification in E-Commerce. ICST Transactions on Scalable Information Systems, 12. https://doi.org/10.4108/eetsis.6789
Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? arXiv preprint. https://doi.org/10.48550/arXiv.2207.08815
Hertati, L., & Yadiati, W. (2024). Exploring Human Capital dalam Tingkat Pendidikan Peran Aplikasi Sistem Informasi Akuntansi Mengatasi Deteksi Fraud pada Aplikasi Shopee. Jurnal Riset Akuntansi, 16(1). https://ojs.unikom.ac.id/index.php/jira/article/download/11457/4283/43018
Karim, A. A. (2025). Analisis Prediktif untuk Mendeteksi Penipuan E-Commerce Menggunakan Algoritma Pembelajaran Mesin. The Indonesian Journal of Computer Science, 14(2). https://doi.org/10.33022/ijcs.v14i2.4202
Kaya, O., Shahen Shah, A. F. M., Karabulut, M. A., Karahan, S. N., Osmanca, M. S., & Acir, N. (2026). Explainable Artificial Intelligence (XAI): Concepts, Applications, Challenges, and Future Perspectives. IEEE Access. https://doi.org/10.1109/ACCESS.2026.3663161
Kustanto, P. (2024). Implementation of a Machine Learning with a Python Programming Approach to Predict the Level of Disease Cases. Global Conference on Innovation and Research (GCIR), 1(1). https://doi.org/10.38035/gcir.v1i1
Lhaura, L., Khairul Anam, M., Firdaus, M. B., Yunefri, Y., & Rahmi, N. A. (2024). Enhancing Machine Learning Model Performance in Addressing Class Imbalance. COGITO Smart Journal, 10(1). https://cogito.unklab.ac.id/index.php/cogito/article/download/626/327
Maresti, F. A., Anugraheni, G. M., Hargiyanto, R. A., & Mustaqim, K. (2024). Penerapan Exploratory Data Analysis (EDA) dan Analisis Recency, Frequency, and Monetary (RFM) untuk Segmentasi Pelanggan E-Commerce. Jurnal Competitive. https://ejurnal.ulbi.ac.id/index.php/competitive/article/download/4059/1405
Miruna, S., Amali, J., Dharshini, K., & Iswarya, A. (2026). Fraud Detection in Online Shopping Using XGBoost. International Journal for Multidisciplinary Research (IJFMR), 7(5). https://www.ijfmr.com/papers/2025/5/58724.pdf
Mohammed, Z. H., Hatem Khorsheed, F., & Ahmed, G. J. (2025). Ensemble Deep Learning Strategy for Handling Imbalanced Credit Card Fraud Data. Journal of Informatics and Computer Science (JOINCS), 8(2). https://joincs.umsida.ac.id/index.php/joincs/article/download/1670/1858
Ponmozhi, K., & Surya, J. (2026). ExhibitorIQ: Intelligent Exhibitor Data Extraction and Event Outreach Platform. Iconic Research and Engineering Journals, 9(9). https://doi.org/10.64388/IREV9I9-1715669
Rahim, N., & Basheer, M. K. P. (2025). A hybrid explainability framework for recommender systems using SHAP with natural language interpretations. International Journal of Innovative Research and Scientific Studies, 8(6), 687–703. https://doi.org/10.53894/ijirss.v8i6.9669
Ranglani, H. (2024). Empirical Analysis of the Bias-Variance Tradeoff Across Machine Learning Models. Machine Learning and Applications: An International Journal, 11(4), 1–12. https://doi.org/10.5121/mlaij.2024.11401
Raufi, B., Finnegan, C., & Longo, L. (2024). A comparative analysis of SHAP, LIME, ANCHORS, and DICE for interpreting a dense neural network in credit card fraud detection. Communications in Computer and Information Science, 2156. https://doi.org/10.1007/978-3-031-63803-9_20
Salsabila, L., & Hutagalung, F. S. (2025). Performance Comparison of Random Forest and XGBOOST Algorithms on E-Commerce App Sentiment Analysis. Indonesian Journal of Applied Technology, 2(2), 57–67. https://jurnal.umsu.ac.id/index.php/Ijatcos/article/download/26211/pdf
Shimaoka, A. M., Ferreira, R. C., & Goldman, A. (2024). The evolution of CRISP-DM for Data Science: Methods, Processes and Frameworks. SBC Reviews on Computer Science, 4(1), 28–43. https://doi.org/10.5753/reviews.2024.3757
Stafeev, A., & Pellegrino, G. (2024). SoK: State of the Krawlers - Evaluating the Effectiveness of Crawling Algorithms for Web Security Measurements. 33rd USENIX Security Symposium (USENIX Security 24). https://www.usenix.org/conference/usenixsecurity24/presentation/stafeev
Tanasă, A.-M., Oprea, S.-V., & Bâra, A. (2023). Web Scraping and Review Analytics. Extracting Insights from Commercial Data. Ovidius University Annals, Economic Sciences Series, 23(2). https://doi.org/10.61801/OUAESS.2023.2.45
Teoh, T. T., & Rong, Z. (2022). Python for Artificial Intelligence. Springer. https://doi.org/10.1007/978-981-16-8615-3_1
Thanathamathee, P., Sawangarreerak, S., Chantamunee, S., & Nizam, D. N. M. (2024). SHAP-Instance Weighted and Anchor Explainable AI: Enhancing XGBoost for Financial Fraud Detection. Emerging Science Journal, 8(6), 2404–2430. https://doi.org/10.28991/ESJ-2024-08-06-016
Wijaya, W. (2025). Exploratory Data Analysis Pada Dataset Ulasan Pelanggan Mc’Donald di Australia. Journal of Smart Technology and Engineering, 1(1). https://doi.org/10.28932/jste.v1i1.13176
Wu, C.-Y., Kuo, C.-C., & Yang, C.-S. (2023). Phishing Detection with Browser Extension Based on Machine Learning. 18th Asia Joint Conference on Information Security (AsiaJCIS), 81–87. https://doi.org/10.1109/AsiaJCIS60284.2023.00023
Yin, A., & Li, B. (2025). XGBoost-Based Fraud Detection and Risk Assessment in E-commerce Transactions. Proceedings of 2025 2nd International Conference on Big Data and Digital Management (ICBDDM 2025), 505–511. https://doi.org/10.1145/3768801.3768881
Zheng, A., & Casari, A. (2024). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. O’Reilly Media.
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