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Sumual, Imanuel Marcell
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Computer Science & IT Decision Sciences, Operations Research & Management

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KLASIFIKASI KATEGORI PRODUK TERLARIS PADA E-COMMERCE MENGGUNAKAN ALGORITMA NAIVE BAYES Sumual, Imanuel Marcell; Supriadi, Jonathan; Effendy, Ellena; Wijaya, Andri
Jurnal Sistem Informasi (JUSIN) Vol 5 No 2 (2024): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v5i2.2893

Abstract

Naive Bayes algorithm used to classify the best-selling product categories in e-commerce. The data used comes from a public Kaggle dataset, comprising 250,000 transactions during the 2020–2023 period. The analysis process follows the CRISP-DM model, including stages such as business understanding, data preparation, modeling, and model evaluation using a confusion matrix. Evaluation results show that the model achieved an accuracy of 92.64%, precision of 91.51%, and recall of 96.68%. The analysis revealed that the best-selling product category is Clothing, followed by Books, Electronics, and Home. This study demonstrates that the Naive Bayes algorithm can be effectively implemented to support stock management and data-driven marketing strategies in e-commerce.
Co-Authors Andri Wijaya Effendy, Ellena Supriadi, Jonathan