Sistem Informasi Deteksi Deepfake Video Promosi Affiliate Menggunakan Arsitektur Inception-ResNet v2
DOI:
https://doi.org/10.31294/icej.v6i2.13694Keywords:
Deepfake, Affiliate Marketing, Inception-ResNet v2Abstract
Maraknya pemanfaatan video pendek dalam platform affiliate marketing saat ini menghadapi ancaman siber baru berupa teknologi deepfake. Manipulasi wajah tokoh publik atau influencer oleh pihak tidak bertanggung jawab untuk mengejar komisi afiliasi berpotensi merusak integritas sistem, merugikan konsumen, dan menurunkan reputasi platform e-commerce. Penelitian ini bertujuan untuk merancang sebuah subsistem tata kelola konten (Content Governance IS) otomatis guna mendeteksi dan memitigasi penyebaran video deepfake promosi produk. Metode yang diusulkan mengintegrasikan algoritma Multi-task Cascaded Convolutional Networks (MTCNN) pada tahap preprocessing untuk mengekstrak Region of Interest (ROI) wajah secara dinamis ke dalam matriks 160 X 160 piksel melalui lingkungan Google Colab. Selanjutnya, klasifikasi biner dilakukan memanfaatkan teknik Transfer Learning berbasis arsitektur Deep Learning Inception-ResNet v2 yang dikombinasikan dengan Global Average Pooling serta lapisan dropout (rate=0.5) untuk mencegah overfitting. Pengambilan keputusan pada sistem informasi ini menerapkan Three-Tier Decision Framework dengan pembagian zona otomatis (Approved, Pending untuk Audit, dan Rejected). Sesuai hipotesis, implementasi model ini mampu menghasilkan deteksi dengan tingkat akurasi yang tinggi serta efisiensi waktu pemrosesan komputasi yang sangat cepat (waktu inferensi < 1 detik per video). Hasil penelitian ini diharapkan dapat memberikan kontribusi signifikan berupa model arsitektur sistem informasi keamanan konten yang tangguh, adaptif, dan siap diintegrasikan sebagai API pada sistem manajemen konten skala besar.
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