Perbandingan Algoritma Naive Bayes, Decision Tree, dan KNN untuk Klasifikasi Produk Populer Adidas US dengan Confusion Matrix

 Lazuardi Firdaus (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)
 (*)Tedy Setiadi Mail (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)

(*) Corresponding Author

Submitted: May 4, 2023; Published: December 22, 2023

Abstract

Adidas America, Inc. (also known as Adidas US) is a company that produces shoes, clothing, and accessories as a subsidiary of Adidas AG, which is known worldwide for its trademark three stripes on its products. Product popularity is very important in increasing sales, especially for products that are frequently purchased, positively reviewed by customers, and reviewed by customers. In this case, there are Adidas US products whose popularity is still unknown. Therefore, in this case, popular Adidas US products will be classified as business needs of Adidas US. The classification algorithm used to classify popular products is Naive Bayes, Decision Tree, and KNN for classifying the popularity of Adidas products in the United States using the CRISP-DM method on the dataset. The data mining process is performed to discover patterns in the data set with the stages of business understanding, data pre-processing, and classification modeling with three different algorithms. The results of the three algorithms are tested with a confusion matrix, and the highest accuracy value is achieved by Decision Tree with 92.42%, making it the best algorithm for classifying popular Adidas US products.

Keywords


Comparison; Classification; Naive Bayes; Decision Tree; KNN

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