Deteksi Penipuan Kartu Kredit Menggunakan Support Vector Machine dengan Optimasi Grid Search dan Genetic Algorithm
-
Lailan Sahrina Hasibuan
*
Institut Pertanian Bogor, Bogor,
Indonesia
- Fatimah Alfiatul Jannah Institut Pertanian Bogor, Bogor, Indonesia (*) Corresponding Author
Abstract
Credit card transactions have increased significantly every year. Along with the increasing use of credit cards, the risk of fraud by irresponsible people also increases. Credit card fraud can be detected with the help of machine learning. The main problem that often encountered is the transaction data has very large dimensions, unbalanced classes, and requires a detection process with a short computation time. Therefore we need a model that can produce good performance with short computation time using the support vector machine (SVM) method with grid search and genetic algorithm optimization. From the three models built, it was found that the SVM model using an initial dataset which was balanced using ADASYN and searching for the best parameters using grid search as a hyperparameter optimization technique was able to carry out good detection and short computing time. This model is able to detect fraudulent transactions with 99% sensitivity and 99% specificity and the shortest model training time among the other two models.
Downloads
References
Bank Indonesia, “Laporan Kelembagaan Bank Indonesia Triwulan 2 2022,” Jakarta, 2022.
S P Maniraj, Aditya Saini, Shadab Ahmed, and Swarna Deep Sarkar, “Credit Card Fraud Detection using Machine Learning and Data Science,” Int. J. Eng. Res., vol. 08, no. 09, pp. 110–115, 2019.
I. Benchaji, S. Douzi, and B. El Ouahidi, Using genetic algorithm to improve classification of imbalanced datasets for credit card fraud detection, vol. 66. Springer International Publishing, 2019.
E.-A. MINASTIREANU and G. MESNITA, “An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection,” Inform. Econ., vol. 23, no. 1/2019, pp. 5–16, 2019.
N. Yousefi, M. Alaghband, and I. Garibay, “A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection,” pp. 1–27, 2019.
J. I.-Z. Chen and K.-L. Lai, “Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert,” J. Artif. Intell. Capsul. Networks, vol. 3, no. 2, pp. 101–112, 2021.
D. Dharmawan, “Deteksi Anomali Pada Transaksi Kartu Kredit Menggunakan Adaptive Synthetic Resampling(ADASYN) dan Support Vector Machine (SVM),” 2022.
C. Li, N. Ding, Y. Zhai, and H. Dong, “Comparative study on credit card fraud detection based on different support vector machines,” Intell. Data Anal., vol. 25, no. 1, pp. 105–119, 2021.
Z. Pooranian, M. Shojafar, R. Tavoli, M. Singhal, and A. Abraham, “A hybrid metaheuristic algorithm for job scheduling on computational grids,” Inform., vol. 37, no. 2, pp. 157–164, 2013.
B. Alhijawi and A. Awajan, “Genetic algorithms: theory, genetic operators, solutions, and applications,” Evol. Intell., vol. 17, no. 3, pp. 1245–1256, Jun. 2023.
“Credit Card Fraud Detection.” [Online]. Available: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud/data. [Accessed: 16-Jun-2024].
N. G. Ramadhan, “Comparative Analysis of ADASYN-SVM and SMOTE-SVM Methods on the Detection of Type 2 Diabetes Mellitus,” Sci. J. Informatics, vol. 8, no. 2, pp. 276–282, 2021.
M. A. Umar and C. Zhanfang, “Effects of Feature Selection and Normalization on Network Intrusion Detection,” pp. 1–25, 2020.
D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl. Soft Comput., vol. 97, p. 105524, Dec. 2020.
H. Henderi, “Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer,” IJIIS Int. J. Informatics Inf. Syst., vol. 4, no. 1, pp. 13–20, 2021.
C. Cortes and V. Vapnik, “Support-Vector Networks,” vol. 297, pp. 273–297, 1995.
A. S. Desuky and S. Hussain, “An Improved Hybrid Approach for Handling Class Imbalance Problem,” Arab. J. Sci. Eng., vol. 46, no. 4, pp. 3853–3864, 2021.
A. Alsarhan, M. Alauthman, E. Alshdaifat, A. R. Al-Ghuwairi, and A. Al-Dubai, “Machine Learning-driven optimization for SVM-based intrusion detection system in vehicular ad hoc networks,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 5, pp. 6113–6122, 2023.
D. J. Kalita, V. P. Singh, and V. Kumar, “A novel adaptive optimization framework for SVM hyper-parameters tuning in non-stationary environment: A case study on intrusion detection system,” Expert Syst. Appl., vol. 213, p. 119189, Mar. 2023.
C. Y. Guo and Y. C. Chou, “A novel machine learning strategy for model selections - Stepwise Support Vector Machine (StepSVM),” PLoS ONE, vol. 15, no. 8 August. 2020.
L. S. Hasibuan, W. A. Kusuma, and W. B. Suwamo, “Identification of single nucleotide polymorphism using support vector machine on imbalanced data,” Proc. - ICACSIS 2014 2014 Int. Conf. Adv. Comput. Sci. Inf. Syst., pp. 375–379, 2014.
S. Bagui and K. Li, “Resampling imbalanced data for network intrusion detection datasets,” J. Big Data, vol. 8, no. 1, 2021.
N. V Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” 2002.
H. He, Y. Bai, E. A. Garcia, and S. Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” Proc. Int. Jt. Conf. Neural Networks, no. 3, pp. 1322–1328, 2008.
L. S. Hasibuan, S. Nabila, N. Hudachair, and M. A. Istiadi, “Evaluation of F-Measure and Feature Analysis of C5.0 Implementation on Single Nucleotide Polymorphism Calling,” Indones. J. Artif. Intell. Data Min., vol. 1, no. 1, p. 1, 2018.
G. Varoquaux and O. Colliot, “Evaluating Machine Learning Models and Their Diagnostic Value,” Neuromethods, vol. 197, pp. 601–630, 2023.
B. D. O’Fallon, W. Wooderchak-Donahue, and D. K. Crockett, “A support vector machine for identification of single-nucleotide polymorphisms from next-generation sequencing data,” Bioinformatics, vol. 29, no. 11, pp. 1361–1366, 2013.
P. Yang, Z. Zhang, B. B. Zhou, and A. Y. Zomaya, “Sample Subset Optimization for Classifying Imbalanced Biological Data,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6635 LNAI, no. PART 2, pp. 333–344, 2011.
H. Mohammedqasim, A. Ahmed Jasim, A. Mohammedqasem, and O. Ata, “Enhancing Predictive Performance in Covid-19 Healthcare Datasets: a Case Study Based on Hyper Adasyn Over-Sampling and Genetic Feature Selection,” J. Eng. Sci. Technol., vol. 19, no. 2, pp. 598–617, 2024.
A. F. Pulungan, D. Selvida, and A. I. Silitonga, “Combination of ADASYN and Random Forest for Classification of Imbalanced Lung Cancer Dataset,” AIP Conf. Proc., vol. 2987, no. 1, Apr. 2024.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Deteksi Penipuan Kartu Kredit Menggunakan Support Vector Machine dengan Optimasi Grid Search dan Genetic Algorithm
Pages: 344-353
Copyright (c) 2024 Lailan Sahrina Hasibuan, Fatimah Alfiatul Jannah
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
