Fraud Detection using Machine Learning in e-Commerce

Abstract: The volume of internet users is increasingly causing transactions on e-commerce to increase as well. We observe the quantity of fraud on online transactions is increasing too. Fraud prevention in e-commerce shall be developed using machine learning, this work to analyze the suitable machine learning algorithm, the algorithm to be used is the Decision Tree, Naive Bayes, Random Forest, and Neural Network. Data to be used is still unbalance. Synthetic Minority Over-sampling Technique (SMOTE) process is to be used to create balance data. Result of evaluation using confusion matrix achieve the highest accuracy of the neural network by 96 percent, random forest is 95 percent, Naïve Bayes is 95 percent, and Decision tree is 91 percent. Synthetic Minority Over-sampling Technique (SMOTE) is able to increase the average of F1-Score from 67.9 percent to 94.5 percent and the average of G-Mean from 73.5 percent to 84.6 percent.

Adi Saputra and Suharjito. “Fraud Detection using Machine Learning in e-Commerce”. International Journal of Advanced Computer Science and Applications (IJACSA) 10.9 (2019). http://dx.doi.org/10.14569/IJACSA.2019.0100943

@article{Saputra2019,
title = {Fraud Detection using Machine Learning in e-Commerce},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100943},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100943},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {9},
author = {Adi Saputra and Suharjito}
}