Komparasi Tujuh Algoritma Identifikasi Fraud ATM Pada PT. Bank Central Asia Tbk
The high use of ATM machines has created a loophole for fraud that can be carried out by third parties in assisting PT. Bank Central Asia Tbk to keep ATM machines ready to use by customers. Not fast and difficult identification of ATM machine fraud is a problem for PT. Bank Central Asia Tbk. With this problem, the researcher collected 5 datasets and pre-processed the dataset so that it could be used for modeling and algorithm testing, in order to answer the problems that occurred. There were seven comparisons of algorithms including decision trees, gradient boosted trees, logistic regression, naive bayes (kernels), naive bayes, random forest and random tree. After modeling and testing, the results show that the gradient boosted trees algorithm is the best algorithm with an accuracy of 99.85% and an AUC value of 1, the high result of this algorithm is due to the compatibility of each attribute tested with the gradient boosted trees character that is driven where this algorithm is save and existing results. So the gradient boosted trees algorithm is the solution to the problems that have arisen by PT. Bank Central Asia Tbk.
 A. Anggraina, R. Primartha, and A. Wijaya, “The combination of logistic regression and gradient boost tree for email spam detection,” J. Phys. Conf. Ser., vol. 1196, no. 1, 2019, doi: 10.1088/1742-6596/1196/1/012013.
 G. Rushin, C. Stancil, M. Sun, S. Adams, and P. Beling, “Horse race analysis in credit card fraud - Deep learning, logistic regression, and Gradient Boosted Tree,” in 2017 Systems and Information Engineering Design Symposium, SIEDS 2017, 2017, doi: 10.1109/SIEDS.2017.7937700.
 A. Bisri and R. Rachmatika, “Integrasi Gradient Boosted Trees dengan SMOTE dan Bagging untuk Deteksi Kelulusan Mahasiswa,” vol. 8, no. 4, 2019.
 PT Bank Central Asia Tbk, “Laporan Tahunan BCA 2018 Keberlanjutan,” 2019.
 D. P. Lingga, Chastine Fatichah, “Deteksi Gempa Berdasarkan Data Twitter Menggunakan Decision Tree, Random Forest, dan SVM,” J. Tek. ITS Vol. 6, No. 1, ISSN 2337-3539, vol. 6, no. 1, p. (2301-9271), 2017, doi: 10.1109/ICBBE.2009.5162818.
 I. Hadjar, “Jurnal phenomenon,” Phenomenon, vol. 07, no. 1, pp. 187–195, 2017.
 T. B. Sasongko and O. Arifin, “Implementasi Metode Forward Selection Pada Algoritma Support Vector Machine ( Svm ) Dan Naive Bayes Classifier Kernel Density ( Studi Kasus Klasifikasi Jalur Minat Sma ) Implementation Forward Selection Methods for Support Vector Machine ( Svm ) and Naïve,” vol. 6, no. 4, pp. 383–388, 2019, doi: 10.25126/jtiik.201961000.
 Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” J. Edik Inform., vol. 2, no. 2, pp. 213–219, 2017.
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