Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal of Informatics Management and Information Technology
Pakpahan, Josua Prayuda
Unknown Affiliation
Computer Science & IT

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

Found 1 Documents
Search

 1 
Klasifikasi Produk Iphone dengan Menggunakan Algoritma XGBoost Sihombing, Stevi Freshia; Pakpahan, Josua Prayuda; Lubis, Andre Hasudungan
Journal of Informatics Management and Information Technology Vol. 5 No. 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i3.649

Abstract

The iPhone has become a symbol of advanced technology and a modern lifestyle that is highly sought after by the global community, including in Indonesia. Known for its stable and exclusive iOS operating system, this product offers seamless cross-device integration, consistent system updates, and high performance through the support of the latest generation of processors. The iPhone also has a visual appeal through a minimalist and elegant design, as well as superior features such as professional camera quality, high-level data security, and power efficiency. The high popularity of the iPhone makes it one of the most competitive products in the smartphone market. However, the diversity of models, features, and prices of each iPhone series causes user preferences to be diverse and complex. In an effort to understand these preferences, an accurate classification method is needed to group products according to consumer appeal. This study adopts the XGBoost algorithm which is known to be effective in handling complex and large data. By utilizing iPhone product sales transaction data in the Indonesian market, this model is designed to identify purchasing patterns and user segmentation. The classification results are expected to provide deeper insights for manufacturers and marketers in formulating more targeted data-based marketing strategies.
Co-Authors Andre Hasudungan Lubis Sihombing, Stevi Freshia