Optimasi Deteksi Penipuan Kartu Kredit Menggunakan Regresi Logistik dengan Particle Swarm Optimization
DOI:
https://doi.org/10.31294/ijcs.v4i2.8984Keywords:
Logistic Regression, Particle Swarm Optimization, PCA, Penipuan Kartu Kredit, SMOTEAbstract
Meningkatnya prevalensi transaksi digital telah menyebabkan lonjakan penipuan kartu kredit, yang memerlukan metode deteksi canggih yang menyeimbangkan akurasi dan efisiensi komputasi. Studi penelitian mengusulkan sistem deteksi penipuan yang dioptimalkan menggunakan Logistic Regression (LR) dengan Particle Swarm Optimization (PSO). Peran untuk mengatasi tantangan ketidakseimbangan kelas dan data berdimensi tinggi, kerangka kerja tersebut menggabungkan Teknik Oversampling Minoritas Sintetis (SMOTE) untuk penyeimbangan data, RobustScaler untuk normalisasi yang tahan terhadap outlier, dan Analisis Komponen Utama (PCA) untuk pengurangan dimensionalitas. Algoritma PSO mengoptimalkan parameter LR (C), meningkatkan generalisasi model dan kinerja deteksi. Eksperimen dilakukan pada kumpulan data Credit Card yang berisi 284.807 transaksi, dengan kasus penipuan hanya mewakili 0,172% dari data ketidakseimbangan kelas yang parah. Model yang diusulkan mencapai akurasi 97,47%, presisi 99,82%, recall 89% (kelas penipuan), dan skor ROC-AUC 0,97, yang menunjukkan kinerja yang unggul dalam membedakan transaksi penipuan. Matriks kebingungan mengungkapkan 110 positif benar (deteksi penipuan yang benar) dengan hanya 13 negatif palsu, yang menunjukkan identifikasi penipuan yang kuat sekaligus meminimalkan alarm palsu. Analisis komparatif di berbagai pemisahan pengujian mengonfirmasi konsistensi model, dengan F1-Score secara konsisten di atas 98,5%. Hasil tersebut menyoroti efektivitas penyetelan hiperparameter berbasis PSO dalam meningkatkan kinerja LR, khususnya dalam kumpulan data yang tidak seimbang. Integrasi SMOTE dan PCA memastikan efisiensi komputasi tanpa mengorbankan kemampuan deteksi. Pendekatan memberi solusi yang dapat diskalakan dan presisi tinggi untuk deteksi penipuan waktu nyata, mengurangi kerugian finansial sekaligus mempertahankan efisiensi operasional.
References
Ab Wahab, M. N., Nefti-Meziani, S., & Atyabi, A. (2015). A comprehensive review of swarm optimization algorithms. PLoS ONE, 10(5), 1–36. https://doi.org/10.1371/journal.pone.0122827
Afriyie, J. K., Tawiah, K., Pels, W. A., Addai-Henne, S., Dwamena, H. A., Owiredu, E. O., Ayeh, S. A., & Eshun, J. (2023). A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions. Decision Analytics Journal, 6(January), 100163. https://doi.org/10.1016/j.dajour.2023.100163
Alarfaj, F. K., Malik, I., Khan, H. U., Almusallam, N., Ramzan, M., & Ahmed, M. (2022). Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms. IEEE Access, 10(April), 39700–39715. https://doi.org/10.1109/ACCESS.2022.3166891
Alatawi, M. N. (2025). Machine Learning with Applications Detection of fraud in IoT based credit card collected dataset using machine learning. Machine Learning with Applications, 19(May 2024), 100603. https://doi.org/10.1016/j.mlwa.2024.100603
Bansal, A., & Garg, H. (2021). An Efficient Techniques for Fraudulent detection in Credit Card Dataset: A Comprehensive study. IOP Conference Series: Materials Science and Engineering, 1116(1), 012181. https://doi.org/10.1088/1757-899x/1116/1/012181
Bin Sulaiman, R., Schetinin, V., & Sant, P. (2022). Review of Machine Learning Approach on Credit Card Fraud Detection. Human-Centric Intelligent Systems, 2(1–2), 55–68. https://doi.org/10.1007/s44230-022-00004-0
Charizanos, G., Demirhan, H., & İçen, D. (2024). An online fuzzy fraud detection framework for credit card transactions. Expert Systems with Applications, 252(April). https://doi.org/10.1016/j.eswa.2024.124127
de Amorim, L. B. V., Cavalcanti, G. D. C., & Cruz, R. M. O. (2023). The choice of scaling technique matters for classification performance. Applied Soft Computing, 133, 1–37. https://doi.org/10.1016/j.asoc.2022.109924
Dornadula, V. N., & Geetha, S. (2019). Credit Card Fraud Detection using Machine Learning Algorithms. Procedia Computer Science, 165, 631–641. https://doi.org/10.1016/j.procs.2020.01.057
Hussein, A. S., Khairy, R. S., Mohamed Najeeb, S. M., & Salim ALRikabi, H. T. (2021). Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression. International Journal of Interactive Mobile Technologies, 15(5), 24–42. https://doi.org/10.3991/ijim.v15i05.17173
Ileberi, E., Sun, Y., & Wang, Z. (2021). Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost. IEEE Access, 9, 165286–165294. https://doi.org/10.1109/ACCESS.2021.3134330
Ileberi, E., Sun, Y., & Wang, Z. (2022). A machine learning based credit card fraud detection using the GA algorithm for feature selection. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-022-00573-8
Itoo, F., Meenakshi, & Singh, S. (2021). Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection. International Journal of Information Technology (Singapore), 13(4), 1503–1511. https://doi.org/10.1007/s41870-020-00430-y
Kilickaya, O. (2024). Credit Card Fraud Detection: Comparison of Different Machine Learning Techniques. International Journal of Latest Engineering and Management Research (IJLEMR), 9(2), 15–27. https://doi.org/10.56581/ijlemr.9.02.15-27
Knn, R. U., & Regression, L. (2023). Credit Card Fraud Detection : An Improved Strategy for High.
Leevy, J. L., Hancock, J., & Khoshgoftaar, T. M. (2023). Comparative analysis of binary and one-class classification techniques for credit card fraud data. Journal of Big Data, 10(1). https://doi.org/10.1186/s40537-023-00794-5
Madhurya, M. J., Gururaj, H. L., Soundarya, B. C., Vidyashree, K. P., & Rajendra, A. B. (2022). Exploratory analysis of credit card fraud detection using machine learning techniques. Global Transitions Proceedings, 3(1), 31–37. https://doi.org/10.1016/j.gltp.2022.04.006
Razaque, A., Frej, M. B. H., Bektemyssova, G., Amsaad, F., Almiani, M., Alotaibi, A., Jhanjhi, N. Z., Amanzholova, S., & Alshammari, M. (2023). Credit Card-Not-Present Fraud Detection and Prevention Using Big Data Analytics Algorithms. Applied Sciences (Switzerland), 13(1). https://doi.org/10.3390/app13010057
Yan, C., Wang, J., Zou, Y., Weng, Y., Zhao, Y., & Li, Z. (2024). Enhancing Credit Card Fraud Detection Through Adaptive Model Optimization.
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