Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Sistem Komputer dan Informatika (JSON)
Firdaus, Lazuardi
Unknown Affiliation
Computer Science & IT Control & Systems Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Sistem Komputer dan Informatika (JSON)

 1 
Perbandingan Algoritma Naive Bayes, Decision Tree, dan KNN untuk Klasifikasi Produk Populer Adidas US dengan Confusion Matrix Firdaus, Lazuardi; Setiadi, Tedy
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.6124

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.
Co-Authors Tedy Setiadi