MENDETEKSI KECURANGAN PADA TRANSAKSI KARTU KREDIT UNTUK VERIFIKASI TRANSAKSI MENGGUNAKAN METODE SVM
Abstract
Credit cards are one of the most popular and many used payment methods on online transactions. In line with the large number of credit card users and even become a daily payment method, the security in verifying every transaction is also very important to be improved. Data mining is one of the most method that can assist in solving problems in credit card transaction security. This research purpose to design a verification system of credit card transactions, which can help the banks in knowing the possibility of fraud that occurred. This research uses support vector machine (SVM) method to detect fraud based on outlier / anomaly on transaction data.Sample data of 100 rows, using attribute of account number as label, month, and transaction nominal as training data. Testing is done by simulating a transaction on the form, by filling in the account number, month, and transaction nominal. The system will detect whether tranksaction with the account number is entered in the designated in the model class. If out of the class then the transaction is suspended.
Keywords
Full Text:
PDFReferences
Devaki R., Kathiresan V., Gunasekaran S., 2014. “Credit Card Fraud Detection using Time Series Analysis”, International Conference on Simulations in Computing Nexus
Nipane V.B., Kalinge P.S., et. all., 2016. “Fraudulent Detection in Credit Card System Using SVM & Decision Tree”, IJSDR Volume 1, Issue 5
Pawar A.D., Kalavadekar P.N., Tambe S.N., 2014. “A Survey on Outlier Detection Techniques for Credit Card Fraud Detection”, IOSR Journal of Computer Engineering, Volume 16, Issue 2
Prasetyo, E., 2012, Data Mining: Konsep dan Aplikasi Menggunakan Matlab, C.V Andi Offset Yogyakarta
Prasetyo, E., 2014, Data Mining: Mengelola Data Menjadi Informasi Menggunakan Matlab, Andi Yogyakarta
Santosa B., 2007, Data Mining: Teknik Pemanfaatan Data Untuk Keperluan Bisnis, Graha Ilmu Yogyakarta
Seeja K.R. & Zareapoor M., 2014. “FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining”, Hindawi Publishing Corporation The Scientific World Journal
Vaishali, 2014. “Fraud Detection in Credit Card by Clustering Approach”, International Journal of Computer Applications (0975-8887) Volume 98-No.3
Zareapoor M., Seeja K.R., Alam M.A., 2012. “Analysis of Credit Card Fraud DetectionTechniques: based on Certain Design Criteria”, International Journal of Computer Applications (0975-8887) Volume 52-No.3
Refbacks
- There are currently no refbacks.