Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Informatics, Electrical and Electronics Engineering
Mochamad Adhari Adiguna
Universitas Pamulang, Tangerang Selatan
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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

Found 1 Documents
Search

 1 
Implementasi Machine learning Untuk Pengambilan Keputusan Pada Paid Traffic Marketplace Shopee Menggunakan Analisis Komparatif Random Forest dan XGBoost Mochamad Adhari Adiguna; Bambang Wisnu Widagdo
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 3 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i3.3021

Abstract

In today's increasingly competitive digital era, paid traffic-based marketing strategies have become one of the primary approaches to improving visibility and sales on e-commerce platforms such as Shopee. However, decision-making related to budget allocation, product selection, and advertisement targeting is still frequently performed manually based on intuition, which may result in suboptimal outcomes. This study aims to implement machine learning as a decision support system for managing paid traffic on the Shopee Marketplace. Historical advertising performance data, including click-through rate (CTR), conversion rate (CR), and return on ad spend (ROAS), were utilized to develop predictive models using the Random Forest and XGBoost algorithms. The developed models were employed to identify advertising performance patterns and provide more effective strategy recommendations based on historical data. Model evaluation was conducted using the accuracy metric to assess its capability in supporting decision-making. The experimental results indicate that the XGBoost model achieved an accuracy of 92.4%, while the Random Forest model achieved 90.8%. Based on these results, XGBoost demonstrated superior performance in supporting decision-making for paid traffic management on the Shopee Marketplace. The findings suggest that the implementation of machine learning can assist business owners in managing paid traffic campaigns more effectively, efficiently, and in a data-driven manner, thereby improving the quality of decision-making within the Shopee Marketplace ecosystem.
Co-Authors Bambang Wisnu Widagdo