{ "date": "2026-05-22", "abstract": "Perkembangan Teknologi Artificial Intelligence(AI) khususnya deepfake\r\nmenimbulkan berbagai permasalahan seperti penyebaran informasi palsu,\r\nmanipulasi media digital, penipuan identitas, dan penyalahgunaan konten visual.\r\nDeepfake mampu memanipulasi wajah manusia pada gambar maupun video\r\nsehingga sulit dibedakan dari konten asli. Kondisi ini dapat menurunkan tingkat\r\nkepercayaan masyarakat terhadap media digital da berpotensi menimbulkan\r\ndampak negatif pada keamanan informasi. Oleh karena itu, diperlukan sistem\r\ndeteksi otomatis yang mampu mengidentifikasi konten deepfake secara akurat.\r\nPenelitian ini bertujuan untuk membangun sistem deteksi deepfake berbasis \r\nConvolutional Neural Network(CNN) menggunakan arsitektur XceptionNet dan\r\nEfficientNet-B0 dengan pendekatan Transfer Learning. Dataset yang digunakan\r\nberasal dari kaggle berupa video Real dan Fake yang kemudian di ekstraksi menjadi\r\nframe gambar wajah. Tahapan penelitian meliputi Preprocessing Data, Resize\r\nImage, Normalisasi, Data Augmentation dan Data Tanpa Augmentation, Training\r\nModel, Fine Tuning, Evaluasi Model, Hingga Implementasi ke aplikasi Web\r\nberbasis Gradio. Evaluasi dilakukan menggunakan metrik accuracy, precision,\r\nrecall, F1-Score, Confusion Matrix, dan Inference Time.\r\n Hasil Penelitian Menunjukan bahwa penggunaan data augmentation dan fine\r\ntuning mampu meningkatkan performa model dalam mendeteksi deepfake. Model\r\nterbaik diperoleh pada XceptionNet dengan augmentation yang menghasilkan\r\naccuracy sebesar 80%, Lebih baik dibandingkan EfficientNet-B0 dengan accuracy\r\nsebesar 77% Penelitian ini berhasil mengimplementasikan klasifikasi sistem deteksi\r\ndeepfake berbasis web yang mampu melakukan klasifikasi gambar dan video secara\r\notomatis. Hasil penelitian ini dapat dimanfaatkan sebagai referensi pengembangan\r\nsistem keamanan digital, vierifikasi media, dan penelitian deteksi deepfake berbasi\r\nDeep Learning.", "lastmod": "2026-09-25 04:29:05", "userid": 12, "eprintid": 33023, "subjects": [ "000.000.006" ], "status_changed": "2026-09-25 04:29:05", "eprint_status": "archive", "creators": [ { "nim": "22.11.5085", "name": { "given": "Nikolaus Gilberto Putra", "lineage": null, "family": "Mantur", "honourific": null } } ], "date_type": "published", "type": "thesis", "thesis_name": "skripsi", "corp_creators": [ "Universitas AMIKOM Yogyakarta" ], "metadata_visibility": "show", "title": "DETEKSI DEEPFAKE PADA FRAME VIDEO\r\nMENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK \r\nDENGAN PERBANDINGAN ARSITEKTUR XCEPTIONNET\r\nDAN EFFICIENTNET-B0", "ispublished": "pub", "dir": "disk0\/00\/03\/30\/23", "contributors": [ { "name": { "honourific": null, "family": "Sasongko", "lineage": null, "given": "Theopilus Bayu" } } ], "datestamp": 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