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      "abstract": "Perkembangan teknologi kecerdasan buatan telah memungkinkan\r\npembuatan konten manipulasi wajah yang dikenal sebagai deepfake, yang\r\nberpotensi menimbulkan dampak serius seperti penyebaran informasi palsu,\r\npenipuan digital, dan ancaman terhadap privasi individu. Meskipun model deep\r\nlearning mampu mendeteksi deepfake dengan akurasi tinggi, sifat black-box dari\r\nmodel tersebut menyebabkan sulitnya memahami proses pengambilan keputusan\r\nyang dihasilkan. Penelitian ini bertujuan untuk mengembangkan sistem deteksi\r\ndeepfake berbasis arsitektur Xception dengan pendekatan transfer learning dan\r\nfine-tuning, serta menerapkan metode Explainable Artificial Intelligence (XAI)\r\nmenggunakan teknik Gradient-weighted Class Activation Mapping (Grad-CAM)\r\nuntuk meningkatkan transparansi model. Dataset yang digunakan adalah CelebDFV2\r\nyang\r\nterdiri\r\ndari\r\n20.000\r\ncitra\r\nwajah\r\nasli\r\ndan\r\ndeepfake,\r\ndengan\r\ntahapan\r\npraproses\r\n\r\nmeliputi\r\nresampling,\r\nface\r\ncropping\r\nmenggunakan\r\nMTCNN,\r\ndan\r\naugmentasi\r\ndata.\r\n\r\nHasil\r\n\r\nevaluasi menunjukkan bahwa model berhasil mencapai akurasi sebesar\r\n93,6%, nilai recall kelas Fake sebesar 0,98, dan nilai AUC sebesar 0,9888, yang\r\nmengindikasikan kemampuan diskriminasi model yang sangat baik. Visualisasi\r\nGrad-CAM membuktikan bahwa model memfokuskan perhatiannya pada area\r\nwajah yang relevan secara semantik dalam proses klasifikasi, sehingga\r\nmeningkatkan interpretabilitas dan kepercayaan terhadap sistem. Hasil penelitian\r\nini dapat dimanfaatkan oleh peneliti, praktisi keamanan digital, jurnalis, serta\r\npenegak hukum dalam upaya verifikasi keaslian konten visual di era kecerdasan\r\nbuatan."
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