DETEKSI PENIPUAN PADA TRANSAKSI KARTU KREDIT DENGAN K-NEAREST NEIGHBOR, RANDOM FOREST, DAN LOGISTIC REGRESSION
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Abstract
The rapid development of technology can provide many conveniences, but with this convenience there are also gaps that can be exploited by certain individuals. One of the problems that often occurs is fraud in credit card transactions which can occur due to theft of credit card data in many ways. That is why it is important to create a system to detect fraudulent credit card transactions. In this research, a comparison of three machine learning algorithms was carried out, namely Random Forest, K-Nearest Neighbor, and Logistic Regression. Imbalance problems in the dataset were also found so resampling was necessary. The resampling methods used are SMOTE and RandomUnderSampler. From the test results it was found that the Random Forest with the original data peforms the best and it can achieve accuracy, precision, recall and F1-score of 100%, 95%, 79% and 85% respectively.
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[1] R. Armiani and E. P. Agustini, "Analisa Fraud Pada Transaksi Kartu Kredit Menggunakan Algoritma Random Forest," Jurnal Teknologi Informasi dan Terapan (J-TIT), vol. 9, no. 2, pp. 118-126, 2022.
[2] A. Kuriawan and Yulianingsih, "Pendugaan Fraud Detection pada kartu kredit dengan Machine Learning," Jurnal Kajian Ilmu dan Teknologi (KILAT), vol. 10, no. 2, pp. 320- 325, 2021.
[3] S. Ruttala, V. Gnaneswar, R. Ramesh and R. G. Ramakoteswara, "Credit Card Fraud Detection Using Machine Learning," in 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, 2020.
[4] V. N. Dornadula and S. Geetha, "Credit Card Fraud Detection using Machine Learning Algorithms," Procedia Computer Science, vol. 165, pp. 631-641, 2019.
[5] N. K. Trivedi, S. Simaiya, U. K. Lilhore and S. K. Sharma, "An Efficient Credit Card Fraud Detection Model Based on Machine Learning Methods," International Journal of Advanced Science and Technology, vol. 19, no. 5, pp. 3414-3424, 2020.
[6] H. Najadat, O. Altiti, A. A. Aqouleh and M. Younes, "Credit Card Fraud Detection Based on Machine and Deep Learning," in 2020 11th International Conference on Information and Communication Systems (ICICS), Irbid, 2020.
[7] A. Roy, J. Sun, R. Mahoney, L. Alonzi, S. Adams and P. Beling, "Deep learning detecting fraud in credit card transactions," in 2018 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, 2018.
[8] M. Febriady, Samsuryadi and D. P. Rini, "Klasifikasi Transaksi Penipuan Pada Kartu Kredit Menggunakan Metode Resampling Dan Pembelajaran Mesin," Jurnal Media Informatika Budidarma, vol. 6, no. 2, pp. 1010-1016, 2022.
[9] N. V. Chawla, K. W. Bowyer, L. O. Hall and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
[10] A. Fernandez, S. Garcia, F. Herrera and N. V. Chawla, "SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary," Journal of Artificial Intelligence Research, vol. 61, pp. 863-905, 2018.
[11] M. Hanafy and R. Ming, "Using Machine Learning Models to Compare Various Resampling Methods in Predicting Insurance Fraud," Journal of Theoretical and Applied Information Technology, vol. 99, no. 12, pp. 2819-2833, 2021.
[12] D. Mediratta and N. Oswal, "Detect Toxic Content to Improve Online Conversations," arXiv preprint arXiv:1911.01217, 2019
[13] D. Varmedja, M. Karanovic, S. Sladojevic, M. Arsenovic and A. Anderla, "Credit Card Fraud Detection - Machine Learning methods," 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), vol. 18, pp. 1-5, 2019.
[14] X. Ying, "An Overview of Overfitting and its Solutions," Journal of Physics: Conference Series, vol. 1168, no. 2, 2019.
[15] Isman, A. Ahmad and A. Latief, "Perbandingan Metode KNN Dan LBPH Pada Klasifikasi Daun Herbal," Jurnal Rekayasa Sistem dan Teknologi Informasi, vol. 5, no. 3, pp. 557- 564, 2019.
[16] F. Tempola, M. Muhammad and A. Khairan, "Perbandingan Klasifikasi Antara Knn Dan Naive Bayes Pada Penentuan Status Gunung Berapi Dengan K-Fold Cross Validation," Jurnal Teknologi Informasi dan Ilmu Komputer (JTII), vol. 5, no. 5, pp. 577-584, 2018.
[17] F. Reviantika, Y. Azhar and G. I. Marthasari, "Analisis Klasifikasi SMS Spam Menggunakan Logistic Regression," Jurnal Sistem Cerdas, vol. 4, no. 3, pp. 155-160, 2021.
[18] F. D. Pramakrisna, F. D. Adhinata and N. A. F. Tanjung, "Aplikasi Klasifikasi SMS Berbasis Web Menggunakan Algoritma Logistic Regression," Jurnal Teknologi Informasi dan Komunikasi, vol. 11, no. 2, pp. 90-97, 2022.
[19] B. P. Pratiwi, A. S. Handayani and Sarjana, "Pengukuran Kinerja Sistem Kualitas Udara Dengan Teknologi WSN Menggunakan Confusion Matrix," Jurnal Informatika UPGRIS, vol. 6, no. 2, pp. 66-75, 2020.
[20] F. Andrianson, Lina and A. Chris, "Pendeteksian Sel Darah Putih Dari Citra Preparat Dengan You Only Look Once," Jurnal Ilmu Komputer dan Sistem Informasi, vol. 9, no. 1, pp. 75-78, 2021.