Identifying Credit Card Fraud in Illegal Transactions Using Random Forest and Decision Tree Algorithms
(1) Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
(2) Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
(3) Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
(4) Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
(5) Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
(6) Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
(7) Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
(8) Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
(*) Corresponding Author
Abstract
The use of credit cards is increasing in today's digital era. This increase has resulted in many cases of fraud which have had a negative impact on credit card owners. To overcome this, many financial institutions have developed credit card fraud detection systems that can identify suspicious transactions. This study uses a classification method, namely random forest and decision tree to identify illegal transactions using a credit card, which then compares the results and attempts to create a model that can be useful for detecting fraud using a credit card that is more accurate and effective. The result of this study is that the accuracy provided by the Decision Tree Classifier is 0.98, while the accuracy provided by the Random Forest Classification is also 0.975. The conclusion obtained that the decision tree has a higher level of accuracy compared to the Random Forest Classification Algorithm, which is 98%. On the other hand, the Random Forest classification algorithm has a slightly lower level of accuracy compared to the Decision Tree classification algorithm, with an accuracy rate of 97.5%
Keywords
Full Text:
PDFReferences
K. Randhawa, C. K. Loo, M. Seera, C. P. Lim, and A. K. Nandi, “Credit Card Fraud Detection Using AdaBoost and Majority Voting,” IEEE Access, vol. 6, pp. 14277–14284, 2018, doi: 10.1109/ACCESS.2018.2806420.
A. De Sá, A. Pereira, and G. Pappa, “A Customized Classification Algorithm for Credit-Card Fraud Detection,” Eng Appl Artif Intell, vol. 72, May 2018, doi: 10.1016/j.engappai.2018.03.011.
Y. Jain, N. Tiwari, S. Dubey, and S. Jain, “A comparative analysis of various credit card fraud detection techniques,” International Journal of Recent Technology and Engineering, vol. 7, pp. 402–407, May 2019.
J. K. Afriyie et al., “A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions,” Decision Analytics Journal, vol. 6, p. 100163, 2023, doi: https://doi.org/10.1016/j.dajour.2023.100163.
H. Wang, C. Ma, and L. Zhou, “A Brief Review of Machine Learning and Its Application,” in 2009 International Conference on Information Engineering and Computer Science, 2009, pp. 1–4. doi: 10.1109/ICIECS.2009.5362936.
J. Qiu, Q. Wu, G. Ding, Y. Xu, and S. Feng, “A survey of machine learning for big data processing,” EURASIP J Adv Signal Process, vol. 2016, no. 1, p. 67, 2016, doi: 10.1186/s13634-016-0355-x.
I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research,” Comput Struct Biotechnol J, vol. 15, pp. 104–116, 2017, doi: https://doi.org/10.1016/j.csbj.2016.12.005.
Q. Dai, C. Zhang, and H. Wu, “Research of Decision Tree Classification Algorithm in Data Mining,” International journal of database theory and application, vol. 9, pp. 1–8, 2016.
J. Han, M. Kamber, and J. Pei, “Data mining concepts and techniques third edition.” Morgan Kaufmann Publishers, Waltham, Mass., 2012. [Online]. Available:http://www.amazon.de/Data-Mining-Concepts-TechniquesManagement/dp/0123814790/ref=tmm_hrd_title_0?ie=UTF8&qid=136603903 &sr=1-1
M. Belgiu and L. Drăguţ, “Random Forest in remote sensing: A review of applications and future directions,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, pp. 24–31, 2016, doi: https://doi.org/10.1016/j.isprsjprs.2016.01.011.
P. Kashyap, Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making, 1st ed. USA: Apress, 2018.
Shah, D and Sharma, LK. "Credit Card Fraud Detection using Decision Tree and Random Forest." ITM Web of Conferences, 2023, search.proquest.com,
.
M. Durairaj and N. Ramasamy, “A comparison of the perceptive approaches for preprocessing the data set for predicting fertility success rate,” International Journal of Control Theory and Applications, vol. 9, no. 27, pp. 255–260, 2016.
E. Alshdaifat, D. Alshdaifat, A. Alsarhan, F. Hussein, and S. M. F. S. El-Salhi, “The Effect of Preprocessing Techniques, Applied to Numeric Features, on Classification Algorithms’ Performance,” Data (Basel), vol. 6, no. 2, 2021, doi: 10.3390/data6020011
S. Cho et al., “A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future,” in Advances in Production Management Systems. Smart Manufacturing for Industry 4.0, I. Moon, G. M. Lee, J. Park, D. Kiritsis, and G. von Cieminski, Eds., Cham: Springer International Publishing, 2018, pp. 311–317.
J. R. Gaikwad, A. B. Deshmane, H. V Somavanshi, S. V Patil, and R.
A. Badgujar, “Credit card fraud detection using decision tree induction algorithm,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 4, no. 6, pp. 2278–3075, 2014.
D. L. Talekar and K. P. Adhiya, “Credit Card Fraud Detection System: A Survey,” International journal of modern engineering research (IJMER), vol. 4, no. 9, 2014.
G. Biau and E. Scornet, “A random forest guided tour,” TEST, vol. 25, no. 2, pp. 197–227, 2016, doi: 10.1007/s11749-016-0481-7.
A. Nurmasani and Y. Pristyanto, “Algoritme Stacking Untuk Klasifikasi Penyakit Jantung Pada Dataset Imbalanced Class,” Pseudocode, vol. 8, no. 1, pp. 21–26, Mar. 2021, doi: 10.33369/pseudocode.8.1.21-26.
C.-S. Yu et al., “Clustering Heatmap for Visualizing and Exploring Complex and High-dimensional Data Related to Chronic Kidney Disease,” J Clin Med, vol. 9, no. 2, 2020, doi: 10.3390/jcm9020403.
Bhavani, T. T., Rao, M. K., & Reddy, A. M. (2019, November). Network intrusion detection system using random forest and decision tree machine learning techniques. In First International Conference on Sustainable Technologies for Computational Intelligence: Proceedings of ICTSCI 2019 (pp. 637-643). Singapore: Springer Singapore.
DOI: https://doi.org/10.32736/sisfokom.v12i3.1730
Refbacks
- There are currently no refbacks.
Indexed By:
