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All Journal Journal of Emerging Information Systems and Business Intelligence (JEISBI)
Maulidia, Ridhotul
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Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Library & Information Science

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Implementation of the Support Vector Machine (SVM) Algorithm in Predicting Transaction Cancellations at Shopee E-commerce: Implementasi Algoritma Support Vector Machine (SVM) Dalam Memprediksi Pembatalan Transaksi Pada E-commerce Shopee Maulidia, Ridhotul; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i1.64414

Abstract

In the digital era, shopping through e-commerce such as Shopee has become increasingly popular. However, transaction cancellation is still an obstacle that causes financial losses for sellers. This research aims to predict transaction cancellation on the Shopee platform using the Support Vector Machine (SVM) algorithm, which is expected to help sellers reduce the risk of loss. The data used comes from the transaction history of Shopee store nafystore.id and is processed using the CRISP-DM method, including business understanding, data preparation, modeling, and deployment. The data preparation process includes cleaning, encoding, normalization, and dimension reduction using Principal Component Analysis (PCA), as well as handling data imbalance with SMOTE. Model testing was conducted using K-Fold Cross-Validation at 3, 5, and 10 folds with different SVM kernels, where the linear kernel showed the best performance with 95.57% accuracy, 95.96% precision, 95.57% recall, and 95.58% F1-Score. The implementation of a web-based system is done using Streamlit to make it easier to use for sellers. The results of this research provide benefits for sellers in identifying cancellation factors, such as Total Payment and Estimated Shipping Fee Deductions. This research not only enriches the application of SVM algorithm in e-commerce analysis, but also provides a reference for other e-commerce platforms to improve transaction efficiency and customer satisfaction.
Co-Authors Wiyli Yustanti