KLASIFIKASI KATEGORI PRODUK TERLARIS PADA E-COMMERCEMENGGUNAKAN ALGORITMA NAIVE BAYES

Dublin Core

Title

KLASIFIKASI KATEGORI PRODUK TERLARIS PADA E-COMMERCEMENGGUNAKAN ALGORITMA NAIVE BAYES

Subject

Classification, Naive Bayes, data mining, best-selling products

Description

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.

Creator

Imanuel Marcell Sumual1, Jonathan Supriadi2, Ellena Effendy3, Andri Wijaya

Source

https://ojs.itb-ad.ac.id/index.php/JUSIN/article/view/2893/639

Publisher

Universitas Katolik Musi Charitas, Palembang

Date

2024

Contributor

Fajar bagus W

Format

PDF

Language

Indonesia

Type

Text

Files

Citation

Imanuel Marcell Sumual1, Jonathan Supriadi2, Ellena Effendy3, Andri Wijaya, “KLASIFIKASI KATEGORI PRODUK TERLARIS PADA E-COMMERCEMENGGUNAKAN ALGORITMA NAIVE BAYES,” Repository Horizon University Indonesia, accessed January 22, 2025, https://repository.horizon.ac.id/items/show/7511.