KLASIFIKASI KATEGORI PRODUK TERLARIS PADA E-COMMERCE MENGGUNAKAN ALGORITMA NAIVE BAYES - Sumual, Imanuel Marcell; Supriadi, Jonathan; Effendy, Ellena; Wijaya, Andri (ITB Ahmad Dahlan Jakarta, 2024)
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.