Penerapan Algoritma Decision Tree Dalam Deteksi Fraud Transaksi Kartu Kredit
Abstract
Credit card fraud poses a serious threat in digital financial systems. Manual detection of suspicious transactions has become ineffective due to detecting fraudulent transactions using the Decision Tree algorithm. The dataset used was obtained from Kaggle and underwent preprocessing and attribute selection. The model was tested under four data split scenarios: 90:10, 80:20, 70:30, and 60:40. Performance evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The results show that the 60:40 data split yielded the best performance, with an accuracy of 97,47%, precision of 86,34%, and recall of 78,67%. These findings indicate that the Decision Tree algorithm can produce highly accurate classification results even without applying data balancing techniques.
Kata kunci: Credit Card; Fraud Detection; Decision Tree; Data Mining.
Abstrak
Penipuan dalam transaksi kartu kredit merupakan ancaman serius dalam sistem keuangan digital. Deteksi secara manual terhadap transaksi yang mencurigakan menjadi tidak efektif seiring dengan meningkatnya volume data. Penelitian ini bertujuan untuk mengembangkan model klasifikasi untuk mendeteksi transaksi fraud menggunakan algoritma Decision Tree C4.5. Dataset yang digunakan diperoleh dari Kaggle dan telah melalui proses praproses dan seleksi atribut. Pengujian dilakukan dengan empat skenario pembagian data training dan data testing, yaitu 90:10, 80:20, 70:30, dan 60:40. Evaluasi performa dilakukan menggunakan confusion matrix dengan metrik akurasi, presisi, dan recall. Hasil menunjukkan bahwa pembagian data 60:40 memberikan performa terbaik dengan nilai akurasi sebesar 97,47%, presisi 86,34%, dan recall 78,67%. Model ini menunjukkan bahwa algoritma Decision Tree mampu memberikan hasil klasifikasi yang sangat baik bahkan tanpa teknik penyeimbangan data.
Kata kunci: Kartu Kredit; Deteksi Penipuan; Decision Tree; Data Mining
References
Ismawati, “Penggunaan Data Mining Untuk Mendeteksi Penipuan Transaksi Kartu Kredit Algoritma Decision Tree,” JAMASTIKA, vol. 4, no.1, pp. 95–101, Apr. 2025, doi: https://doi.org/10.35473/jamastika.v4i1.3632.
D. Hendarsyah, S. Tinggi, I. Ekonomi, and S. Bengkalis, “Analisis Perilaku Konsumen Dan Keamanan Kartu Kredit Perbankan,” Jurnal Perbankan Syariah, vol. 1, no. 1, pp. 85–96, Apr. 2020, doi: https://doi.org/10.46367/jps.v1i1.204.
W. Nugraha, D. Risdiansyah, D. Purwaningtias, T. Hidayatulloh, and S. Suhada, “Kombinasi Tomek-Link Dan Smote Untuk Mengatasi Ketidakseimbangan Kelas Pada Credit Card Fraud,” Jurnal Larik: Ladang Artikel Ilmu Komputer, vol. 2, No.2, no. 2, pp. 32–40, Dec. 2022, doi: 10.31294/larik.v2i2.1789.
A. Joshi, “Decision Tree Algorithm for Credit Card Fraud Detection,” Webology, vol. 18, No. 4, pp. 2055–2061, 2021, doi: 10.29121/web/v18i4/103.
T. Kumar, “Comparison of Logistic Regression and Decision Tree method for Credit Card Fraud Detection,” Int J Res Appl Sci Eng Technol, vol. 9, no. 5, pp. 680–683, May 2021, doi: 10.22214/ijraset.2021.34241.
I. Werdiningsih et al., “Identifying Credit Card Fraud in Illegal Transactions Using Random Forest and Decision Tree Algorithms,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 12, no. 3, pp. 477–484, Nov. 2023, doi: 10.32736/sisfokom.v12i3.1730.
P. T. S. Ningsih, M. Gusvarizon, and R. Hermawan, “Analisis Sistem Pendeteksi Penipuan Transaksi Kartu Kredit dengan Algoritma Machine Learning,” Jurnal Teknologi Informatika dan Komputer, vol. 8, no. 2, pp. 386–401, Sep. 2022, doi: 10.37012/jtik.v8i2.1306.
Hammed and S. Jumoke, “An implementation of decision tree algorithm augmented with regression analysis for fraud detection in credit card,” International Journal of Computer Science and Information Security, vol. 18, no. 2, pp. 79–88, Feb. 2020, Accessed: Mar 29, 2025. [Online]. Available: https://sites.google.com/site/ijcsis/
L. Darell Perwara and F. A. Bachtiar, “Penerapan Algoritma Decision Tree C4.5 Untuk Deteksi Fraud Pada Kartu Kredit dengan Oversampling Synthetic Minority Technique (SMOTE),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 4, no. 8, pp. 2664–2669, Aug. 2020, Accessed: Mar 29, 2025. [Online]. Available: http://j-ptiik.ub.ac.id
S. Wijaya, W. Wesly, K. Ginting, and A. Dharma, “Analysis of Credit Card Fraud Detection Performance Using Random Forest Classifier & Neural Networks Model,” Engineering and Technology Journal, vol. 9, no. 02, pp. 3516–3520, Feb. 2024, doi: 10.47191/etj/v9i02.11.
E. F. Malik, K. W. Khaw, B. Belaton, W. P. Wong, and X. Chew, “Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture,” Mathematics, vol. 10, no. 9, pp. 1–16, May 2022, doi: 10.3390/math10091480.
A. Kurniawan and Y. Yulianingsih, “Pendugaan Fraud Detection pada kartu kredit dengan Machine Learning,” KILAT, vol. 10, Number 2, no. 2, pp. 320–325, Oct. 2021, doi: 10.33322/kilat.v10i2.1482.
D. Shah and L. Kumar Sharma, “Credit Card Fraud Detection using Decision Tree and Random Forest,” ITM Web of Conferences, vol. 53, no. ICDSIA-2023, pp. 2–12, 2023, doi: 10.1051/itmconf/20235302012.
D. Elisa Sinaga et al., “KLIK: Kajian Ilmiah Informatika dan Komputer Analisis Data Mining Algoritma Decision Tree Pada Prediksi Persediaan Obat (Studi Kasus : Apotek Franch Farma),” Kajian Ilmiah Informatika dan Komputer, vol. 2, no. 4, pp. 123–131, Feb. 2022, Accessed: May 25, 2025. [Online]. Available: https://djournals.com/klik
P. Tiwari, S. Mehta, N. Sakhuja, J. Kumar, and A. K. Singh, “Credit Card Fraud Detection using Machine Learning: A Study,” Techincal Report, pp. 2–10, Aug. 2021, Accessed: May 25, 2025. [Online]. Available: http://arxiv.org/abs/2108.10005
Ainurrohmah, “Akurasi Algoritma Klasifikasi pada Software Rapidminer dan Weka,” Prosiding Seminar Nasional Matematika, vol. 4, No. 1, pp. 493–499, 2021, doi: https://journal.unnes.ac.id/sju/index.php/prisma/.
T. S. Lestari and D. A. N. Sirodj, “Klasifikasi Penipuan Transaksi Kartu Kredit Menggunakan Metode Random Forest,” Jurnal Riset Statistika, vol. 1, no. 2, pp. 160–167, Feb. 2022, doi: 10.29313/jrs.v1i2.525.
A. I. Sang, E. Sutoyo, and I. Darmawan, “Analisis Data Mining Untuk Klasifikasi Data Kualitas Udara Dki Jakarta Menggunakan Algoritma Decision Tree Dan Support Vector Machine Data Minning”, e-Proceeding of Engineering, vol. 8, no. 5, pp. 8954–8963, Oct. 2021, Accessed: May 29, 2025. [Online]. Available: https://openlibrary.telkomuniversity.ac.id/pustaka/170546/analisis-data-mining-untuk-klasifikasi-data-kualitas-udara-dki-jakarta-menggunakan-algoritma-decision-tree-dan-support-vector-machine.html
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