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All Journal Journal of Applied Data Sciences
Anni Karimatul Fauziyyah
Faculty Computer, Department of Informatics, Alma Ata University, Yogyakarta, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Semi-Supervised Classification on Credit Card Fraud Detection using AutoEncoders Nur Rachman Dzakiyullah; Andri Pramuntadi; Anni Karimatul Fauziyyah
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.16

Abstract

The use of credit cards for online purchases has increased dramatically and led to an explosion in credit card fraud. Credit card companies need to be able to identify fraudulent credit card transactions so that customers are not charged for items they do not buy. In this study, we will use semi-supervised learning and combine it with AutoEncoders to identify fraudulent credit card transactions. In this paper, we will implement the use of T-SNE to visualize fraud and non-fraud transactions, then improve the visualization using autoencoders. Classification report proved that it is possible to achieve very acceptable precision using semi-supervised classification to detect credit card fraud.
Co-Authors Andri Pramuntadi Nur Rachman Dzakiyullah