Fraud Detection using Machine Learning in e-Commerce

被引：0

作者：

Saputra, Adi ^{[1
]}

Suharjito ^{[1
]}

机构：

[1] Bina Nusantara Univ Jakarta, Comp Sci Dept, BINUS Grad Program, Comp Sci, Jakarta 11480, Indonesia

来源：

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | 2019年 / 10卷 / 09期

关键词：

Machine learning; random forest; Naive Bayes; SMOTE; neural network; e-commerce; confusion matrix; G-Mean; F1-score; transaction; fraud; DECISION TREE; ADABOOST;

D O I：

10.14569/ijacsa.2019.0100943

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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, Naive 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.

引用

页码：332 / 339

页数：8

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