Implementasi Algoritma Neural Network untuk Deteksi Penipuan Transaksi Kartu Kredit
DOI:
https://doi.org/10.22441/fifo.2026.v18i1.008Abstract
Penelitian ini mengevaluasi performa lima algoritma supervised learning untuk deteksi penipuan kartu kredit menggunakan dataset 690 data dari Kaggle dengan teknik Random Oversampling (ROS). Model seperti k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Logistic Regression, Neural Network, dan Ensemble menunjukkan tingkat akurasi rata-rata antara 80% hingga 90% dalam mendeteksi penipuan. Kontribusi penelitian ini adalah menyediakan perbandingan sistematis beberapa algoritma klasifikasi pada dataset yang sama dengan teknik penyeimbangan data. Hasil uji coba dengan teknik random oversampling menunjukkan bahwa Neural Network (aktivasi SELU dan RELU), mencapai kinerja terbaik dengan accuracy 90%, precision 86%, recall 94%, dan nilai f1-score 90%. Pendekatan Neural Network dengan random oversampling terbukti efektif dalam meningkatkan ketepatan prediksi terhadap penipuan dalam transaksi finansial dibandingkan dengan pendekatan tanpa penggunaan sampling. Keterbatasan penelitian ini adalah ukuran dataset yang kecil (690 data) yang dapat mempengaruhi kemampuan generalisasi model.
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P. T. S. Ningsih, M. Gusvarizon, and R. Hermawan, “Analisis Sistem Pendeteksi Penipuan Transaksi Kartu Kredit dengan Algoritma Machine Learning,” J. Teknol. Inform. dan Komput., vol. 8, no. 2, pp. 386–401, 2022, doi: 10.37012/jtik.v8i2.1306.
R. Armiani and E. P. Agustini, “Analisa Fraud Pada Transaksi Kartu Kredit Menggunakan Algoritma Random Forest,” J. Teknol. Inf. dan Terap., vol. 9, no. 2, pp. 118–126, 2022, doi: 10.25047/jtit.v9i2.297.
F. Zamachsari and N. Puspitasari, “Penerapan Deep Learning dalam Deteksi Penipuan Transaksi Keuangan Secara Elektronik,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 203–212, 2021, doi: 10.29207/resti.v5i2.2952.
T. S. Lestari and D. A. N. Sirodj, “Klasifikasi Penipuan Transaksi Kartu Kredit Menggunakan Metode Random Forest,” J. Ris. Stat., vol. 1, no. 2, pp. 160–167, 2022, doi: 10.29313/jrs.v1i2.525.
A. F. Riany and G. Testiana, “Penerapan Data Mining untuk Klasifikasi Penyakit Stroke Menggunakan Algoritma Naïve Bayes,” J. SAINTEKOM, vol. 13, no. 1, pp. 42–54, 2023, doi: 10.33020/saintekom.v13i1.352.
S. Diantika, “Penerapan Teknik Random Oversampling Untuk Mengatasi Imbalance Class Dalam Klasifikasi Website Phishing Menggunakan Algoritma Lightgbm,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 19–25, 2023, doi: 10.36040/jati.v7i1.6006.
Muhammad Haris Diponegoro, Sri Suning Kusumawardani, and Indriana Hidayah, “Tinjauan Pustaka Sistematis: Implementasi Metode Deep Learning pada Prediksi Kinerja Murid,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 10, no. 2, pp. 131–138, 2021, doi: 10.22146/jnteti.v10i2.1417.
E. R. Alfiyyah, R. Andreswari, and E. Sutoyo, “Analisis dan deteksi fraud pada data panggilan menggunakan algoritma k-nearest neighbor (studi kasus: pt xyz),” e-Proceeding Eng., vol. 7, no. 2, pp. 6640–6646, 2020.
A. Nugroho, M. A. Soeleman, R. A. Pramunendar, A. Affandy, and A. Nurhindarto, “Peningkatan Performa Ensemble Learning pada Segmentasi Semantik Gambar dengan Teknik Oversampling untuk Class Imbalance,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 4, pp. 899–908, 2023, doi: 10.25126/jtiik.20241046831.
M. R. Romadhon and F. Kurniawan, “A Comparison of Naive Bayes Methods, Logistic Regression and KNN for Predicting Healing of Covid-19 Patients in Indonesia,” 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, pp. 41–44, 2021, doi: 10.1109/EIConCIT50028.2021.9431845.
W. I. Sabilla and C. Bella Vista, “Implementasi SMOTE dan Under Sampling pada Imbalanced Dataset untuk Prediksi Kebangkrutan Perusahaan,” J. Komput. Terap., vol. 7, no. 2, pp. 329–339, 2021, doi: 10.35143/jkt.v7i2.5027.
A. Kusuma and H. Nurramdhani Irmanda, “Analisis Sentimen Pada Ulasan Aplikasi Indodax di Google Play Store Menggunakan Metode Support Vector Machine,” 2022.
S. A. P. Perdana, T. Bharata Aji, and R. Ferdiana, “Aspect Category Classification dengan Pendekatan Machine Learning Menggunakan Dataset Bahasa Indonesia (Aspect Category Classification with Machine Learning Approach Using Indonesian Language Dataset),” J. Nas. Tek. Elektro dan Teknol. Inf. |, vol. 10, no. 3, pp. 229–235, 2021.
S. Clara, D. Laksmi Prianto, R. Al Habsi, E. Friscila Lumbantobing, and N. Chamidah, “Implementasi Seleksi Fitur Pada Algoritma Klasifikasi Machine Learning Untuk Prediksi Penghasilan Pada Adult Income Dataset,” Semin. Nas. Mhs. Ilmu Komput. dan Apl. Jakarta-Indonesia, vol. 2, no. 1, pp. 741–747, 2021.
Credit-Card-Fraud-Detection: Dataset. Retrieved Juni 26, 2023, from https://www.kaggle.com/datasets/rezasemyari/credit-card-fraud-detection. Kaggle (2023, Juni 26).
Y. Sahin and E. Duman, “Detecting Credit Card Fraud by ANN and Logistic Regression,” in Proc. Int. Symp. Innovations Intell. Syst. Appl., 2011, pp. 315–319, doi: 10.1109/INISTA.2011.5946108.
A. Dal Pozzolo, O. Caelen, R. A. Johnson, and G. Bontempi, “Calibrating Probability with Undersampling for Unbalanced Classification,” in Proc. IEEE Symp. Comput. Intell. Data Mining, 2015, pp. 1–8, doi: 10.1109/CIDM.2015.7415158.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” J. Artif. Intell. Res., vol. 16, pp. 321–357, 2002, doi: 10.1613/jair.953.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016. [Online]. Available: https://www.deeplearningbook.org
S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data Mining for Credit Card Fraud: A Comparative Study,” Decis. Support Syst., vol. 50, no. 3, pp. 602–613, 2011, doi: 10.1016/j.dss.2010.08.008.
G. Ke et al., “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” in Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 3146–3154. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html
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