Analisis Efektivitas Algoritma Pembelajaran Mesin dalam Deteksi Penipuan Transaksi Online
Kata Kunci:
Deteksi penipuan, Random Forest, Neural Networks, pembelajaran mesin, transaksi onlineAbstrak
Penipuan transaksi online merupakan ancaman serius dengan dampak finansial yang signifikan. Penelitian ini menganalisis efektivitas dua algoritma pembelajaran mesin, yaitu Random Forest (RF) dan Neural Networks (NN), dalam mendeteksi transaksi penipuan. Dataset yang digunakan terdiri dari 100.000 transaksi (10% di antaranya fraud) dari TranSecure Database 2023. Hasil eksperimen menunjukkan bahwa NN mencapai kinerja terbaik dengan akurasi 95%, presisi 0.93, dan recall 0.90, sedangkan RF menghasilkan akurasi 93%, presisi 0.91, dan recall 0.89. Tantangan utama meliputi kebutuhan data berkualitas tinggi dan adaptasi terhadap pola penipuan yang dinamis. Temuan ini membuktikan bahwa NN lebih unggul dalam menangani pola kompleks, meskipun memerlukan sumber daya komputasi lebih besar. Penelitian ini memberikan kontribusi praktis dalam pengembangan sistem deteksi penipuan yang lebih robust di industri keuangan digital.
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https://jurnal.polgan.ac.id/index.php/jmp/article/view/14186
https://onlinelibrary.wiley.com/doi/10.1155/2023/8134627
https://www.researchgate.net/publication/347411268_Finance_Fraud_Detection_With_Neural_Network
