Implementasi Model Pendekatan Machine Learning untuk Deteksi Fraud pada Transaksi Pembayaran Digital Paper.Id
Keywords:
Machine Learning, Fraud Detection, Digital Transactions, Anomaly Analysis.
Abstract
This study aims to apply a machine learning approach in detecting fraud in digital payment transactions in the Paper.id platform. The study was conducted using the Research and Development (R&D) method with the Cross-Industry Standard Process for Data Mining (CRISP DM) development model, which consists of six main stages, namely business understanding, data understanding, data preparation, modeling, evaluation, and implementation. The analysis process involves data exploration, feature engineering, and the application of anomaly detection techniques and network analysis. The results of the study show that the application of the machine learning approach is significantly able to identify suspicious transaction patterns such as collusion between users, misuse of promotions, and other unusual transactions. The implementation of this system is expected to improve the accuracy of fraud detection, efficiency of transaction data processing, and strengthen data security and user trust in the Paper.id platform.
References
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Montgometry, D. C. (2019). Introduction to Statistical Quality Control (8th ed.). Wiley.
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Santoso, P., Abijono, A., & Anggreini, N. L. (2021). Algoritma Supervised Learning Dan Unsupervised Learning Dalam Pengolahan Data. Jurnal Teknologi Terapan: G-Tech, 4(2), 315–318. https://doi.org/10.33379/gtech.v4i2.635
Simeone, O. (2018). A Brief Introduction to Machine Learning for Engineers. In Foundations and Trends in Signal Processing (Vol. 12, Issues 3–4). https://doi.org/10.1561/2000000102
Sugiyono. (2014). Metode penelitian bisnis: pendekatan kuantitatif, kualitatif, kombinasi, dan R&D. Alfabeta.
Wang, C., Dou, Y., Chen, M., Chen, J., Liu, Z., & Yu, P. S. (2021). Fraud Detection Using Network Analysis and Machine Learning Techniques. Journal of Financial Technology, 1, 1–4. https://arxiv.org/abs/2110.01171
Watson, H. J., Grecich, D. G., Shearer, C., Hammer, K., Herdlein, S. a, Moncla, B., Davidovic, G., Fong, J., Wong, H. K., & Fong, A. (2000). The CRISP-DM Model: The New Blueprint for Data Mining. Journal of Data Warehousing, 5(4), 15–18.
Wibowo, A., Darwati, I., & Irnawati, O. (2020). Prediksi Operasi Sesar Dengan Machine Learning. J I M P - Jurnal Informatika Merdeka Pasuruan, 4(3). https://doi.org/10.37438/jimp.v4i3.228
Yosephine, V. S., Hanna, T., Setiawati, M., & Setiawan, A. (2023). Machine Learning for Quality Control in Traditional Textile Manufacturing. Jurnal Rekayasa Sistem Industri, 13(1), 165–174. https://doi.org/10.26593/jrsi.v13i1.7173.165-174
Zhang, Z., Chen, L., Liu, Q., & Wang, P. (2020). A Fraud Detection Method for Low-Frequency Transaction. IEEE Access, 8, 25210–25220. https://doi.org/10.1109/ACCESS.2020.2970614
Zheng, A., & Casari, A. (2018). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientist. O’Reilly Media.
Albrecht, C. C., Albercht, W. S., & Dunn, J. G. (2019). Fraud Examination. 6th ed. Cengage Learning.
Ardhitha, R., Anugerah, R., & Sutabri, T. (2025). Analisis Penerapan Machine Learning dan Algoritma Anomali untuk Deteksi Penipuan pada Transaksi Digital. 1, 80–90.
Barabási. (2016). Network Science. Cambridge University Press.
Brownlee, J. (2016). Feature Selection For Machine Learning in Python. https://machinelearningmastery.com/feature-selection-machine-learning-python/
Cressey, D. R. (1953). Other People’s Money. Patterson Smith.
Deloitte. (2022). Southeast Asia Fintech Report: Challenges and Opportunities in Fraud Preverention. Delloitte Insights.
Domingos, P. (2012). A Few Useful Things to Know about Machine Learning. Communications of the ACM, 55(10), 78–87. https://doi.org/10.1145/2347736.2347755
Eldo, H., Ayuliana, A., Suryadi, D., Chrisnawati, G., & Judijanto, L. (2024). Penggunaan Algoritma Support Vector Machine ( SVM ) Untuk Deteksi Penipuan pada Transaksi Online. Jurnal Minfo Polgan, 13(2), 1627–1632. https://doi.org/10.33395/jmp.v13i2.14186
Febriyani, W., Supratman, N. A., & Witjaksono, R. W. (2024). Exploring the Contribution of Fintech to Digital Transformation in Indonesian MSMEs : A Literature Review. 13(6), 2564–2572. https://doi.org/10.32520/stmsi.v13i6.4638
Gee, J. (2014). The Financial Fraud Handbook: Fraud Preverention and Detection Strategies. Wiley.
Guyon, I., & Elisseeff, A. (2003). An Introduction to Variable and Feature Selection 1 Introduction. Journal of Machine Learning Research (JMLR), 3, 1157–1182.
Japit, S., Risyani, Y., Selamat, T., Bombongan, C., & Yuliana, Y. (2024). Deteksi Anomali Transaksi E-Commerce Menggunakan Support Vector Machine Berbasis Data Mining. Jurnal Minfo Polgan, 13(2), 1976–1980.
McKinsey, G. I. (2016). Digital Finance for All : Powering Inclusive Growth in Emerging Economies (Issue September). McKinsey & Company.
Montgometry, D. C. (2019). Introduction to Statistical Quality Control (8th ed.). Wiley.
Newman, M. E. J. (2003). The structure and function of complex networks. In SIAM Review (Vol. 45, Issue 2). Society for Industrial and Applied Mathematics (SIAM).
Nurhayati, Busman, & Iswara, R. P. (2019). PENGEMBANGAN ALGORITMA UNSUPERVIS
ED LEARNING TECHNIQUE PADA BIG DATA ANALYSIS DI MEDIA SOSIAL SEBAGAI MEDIA PROMOSI. Jurnal Teknik Informatika, 12(1), 79–96. https://doi.org/10.15408/jti.v12i1.11342
Otoritas Jasa Keuangan (OJK) Dan Kementrian Komunikasi dan Informatika (Kominfo). (2024). Data Tren Penipuan dalam Transaksi Digital di Indonesia. Di Akses Pada Januari 2025.
Pamungkas, F. S., Prasetya, B. D., & Kharisudin, I. (2020). Perbandingan Metode Klasifikasi Supervised Learning pada Data Bank Customers Menggunakan Python. PRISMA, Prosiding Seminar Nasional Matematika, 3, 692–697. https://journal.unnes.ac.id/sju/index.php/prisma/article/view/37875
Qiang, W., & Zhongli, Z. (2011). Reinforcement learning model, algorithms and its application. Proceedings 2011 International Conference on Mechatronic Science, Electric Engineering and Computer, MEC 2011, 1143–1146. https://doi.org/10.1109/MEC.2011.6025669
Rahardjo, A., Setiawan, D., & Lestari, P. (2021). ingkat Kesadaran Pengguna terhadap Keamanan Transaksi Digital di Indonesia: Sistem Deteksi Penipuan pada Beberapa Platform. Jurnal Riset Keuangan Dan Akuntansi, 7(2), 134–142.
Rauhan, A. (2019). Pengolahan Data Menggunakan Machine Learning. Student Paper Pertamina University, 021, 1–4. https://library.universitaspertamina.ac.id/xmlui/bitstream/handle/123456789/162/Jur
Retnoningsih, E., & Pramudita, R. (2020). Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python. Bina Insani Ict Journal, 7(2), 156. https://doi.org/10.51211/biict.v7i2.1422
Sallu, S.-, & Qammaddin, Q. (2020). Keamanan Data Pembelajaran Online Jaringan Komputer di Perguruan Tinggi. Instruksional, 2(1), 239–244. https://doi.org/10.24853/Instruksional.2.1.35-40
Santoso, P., Abijono, A., & Anggreini, N. L. (2021). Algoritma Supervised Learning Dan Unsupervised Learning Dalam Pengolahan Data. Jurnal Teknologi Terapan: G-Tech, 4(2), 315–318. https://doi.org/10.33379/gtech.v4i2.635
Simeone, O. (2018). A Brief Introduction to Machine Learning for Engineers. In Foundations and Trends in Signal Processing (Vol. 12, Issues 3–4). https://doi.org/10.1561/2000000102
Sugiyono. (2014). Metode penelitian bisnis: pendekatan kuantitatif, kualitatif, kombinasi, dan R&D. Alfabeta.
Wang, C., Dou, Y., Chen, M., Chen, J., Liu, Z., & Yu, P. S. (2021). Fraud Detection Using Network Analysis and Machine Learning Techniques. Journal of Financial Technology, 1, 1–4. https://arxiv.org/abs/2110.01171
Watson, H. J., Grecich, D. G., Shearer, C., Hammer, K., Herdlein, S. a, Moncla, B., Davidovic, G., Fong, J., Wong, H. K., & Fong, A. (2000). The CRISP-DM Model: The New Blueprint for Data Mining. Journal of Data Warehousing, 5(4), 15–18.
Wibowo, A., Darwati, I., & Irnawati, O. (2020). Prediksi Operasi Sesar Dengan Machine Learning. J I M P - Jurnal Informatika Merdeka Pasuruan, 4(3). https://doi.org/10.37438/jimp.v4i3.228
Yosephine, V. S., Hanna, T., Setiawati, M., & Setiawan, A. (2023). Machine Learning for Quality Control in Traditional Textile Manufacturing. Jurnal Rekayasa Sistem Industri, 13(1), 165–174. https://doi.org/10.26593/jrsi.v13i1.7173.165-174
Zhang, Z., Chen, L., Liu, Q., & Wang, P. (2020). A Fraud Detection Method for Low-Frequency Transaction. IEEE Access, 8, 25210–25220. https://doi.org/10.1109/ACCESS.2020.2970614
Zheng, A., & Casari, A. (2018). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientist. O’Reilly Media.
Published
2025-09-30
How to Cite
Syah Husin, L. S., Febri Darmayanti, E., & Nusantoro, J. (2025). Implementasi Model Pendekatan Machine Learning untuk Deteksi Fraud pada Transaksi Pembayaran Digital Paper.Id. Expensive: Jurnal Akuntansi Dan Keuangan, 4(3), 362-375. https://doi.org/10.24127/exclusive.v4i3.10396
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