Ningrum, Wahyu Ajeng Shintia (2026) Analisis Perbandingan Prediksi Kategori Keterjualan Produk Menggunakan Algoritma Decision Tree (C4.5) dan Naive Bayes (Studi Kasus: Petshop Bay Bay Sidoarjo). Undergraduate thesis, Universitas Hayam Wuruk Perbanas.
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Abstract
This study aims to compare the performance of the Decision Tree (C4.5) and Naïve Bayes algorithms in classifying best-selling products at Petshop Bay Bay Sidoarjo based on historical sales data. Product classification is conducted to group the level of product sales into three classes, namely high, medium, and low, in order to support decision-making related to inventory management and sales strategies. The data used in this study consist of historical sales data of pet products from January 2022 to December 2024, obtained directly from Petshop Bay Bay Sidoarjo. Data processing and model testing were carried out using the RapidMiner software, including data preprocessing, model construction, and performance evaluation. The performance of the algorithms was measured using accuracy, weighted mean precision, and weighted mean recall metrics. The results of this study indicate that the Decision Tree (C4.5) algorithm demonstrates better performance than the Naïve Bayes algorithm based on the evaluation results obtained. Therefore, the Decision Tree (C4.5) algorithm is considered more effective for classifying best-selling products at Petshop Bay Bay Sidoarjo. The findings of this study are expected to serve as a reference for business practitioners in implementing data mining techniques to support data-driven decision-making. Keywords: Data Mining; Decision Tree (C4.5); Naïve Bayes; Product Classification; RapidMiner.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Subjects: | 000 - COMPUTER SCIENCE, INFORMATION, GENERAL WORKS > 000 - 009 COMPUTER SCIENCE, INFORMATION, GENERAL WORKS > 005 - COMPUTER PROGRAMMING, PROGRAMS & DATA |
| Divisions: | Bachelor of Information Systems |
| Depositing User: | WAHYU AJENG SHINTIA NINGRUM |
| Date Deposited: | 14 Apr 2026 04:20 |
| Last Modified: | 14 Apr 2026 04:20 |
| URI: | http://eprints.perbanas.ac.id/id/eprint/14233 |
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