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      "abstract": "Pengenalan karakteristik demografis pengguna, seperti usia dan gender,\r\nmelalui suara merupakan tantangan krusial dalam pengembangan sistem interaksi\r\nmanusia dan mesin yang lebih personal dan aman. Namun, penggunaan model\r\nDeep Learning arsitektur tunggal seringkali memiliki keterbatasan, di mana model\r\nCNN kurang optimal dalam menangkap urutan waktu, sedangkan model\r\nsekuensial seperti RNN sering kehilangan detail spasial pada data suara yang\r\nkompleks. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan\r\nmengembangkan model Hybrid yang mengintegrasikan Convolutional Neural\r\nNetwork (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), dan\r\nTransformer. Metode penelitian diawali dengan pengolahan dataset Common\r\nVoice menjadi representasi Mel-Spectrogram, penerapan teknik standardisasi\r\naudio, serta perancangan arsitektur gabungan di mana CNN mengekstraksi fitur\r\nvisual spektral, Bi-LSTM menangkap konteks temporal dua arah, dan\r\nTransformer memfokuskan atensi pada fitur global yang dominan. Hasil\r\npengujian membuktikan bahwa model usulan mampu mencapai akurasi akhir\r\nsebesar 91%, unggul dibandingkan model tunggal Bi-LSTM (74%) dan CNN\r\nstandar (84%). Keberhasilan ini menegaskan bahwa sinergi ketiga algoritma\r\ntersebut merupakan pendekatan yang efektif untuk mengatasi variabilitas\r\nkarakteristik suara manusia yang beragam. Implementasi sistem ini diharapkan\r\ndapat dimanfaatkan secara luas untuk meningkatkan keandalan sistem keamanan\r\nbiometrik, personalisasi layanan e-commerce, serta alat bantu investigasi forensik\r\ndigital.",
      "keywords": "Klasifikasi Suara, Deep Learning, Arsitektur Hybrid, Usia dan\r\nGender, Mel-Spectrogram, Voice Classification, Hybrid Architecture, Age and\r\nGender.",
      "rev_number": 16,
      "institution": "Universitas AMIKOM Yogyakarta",
      "subjects": [
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      "title": "KLASIFIKASI USIA DAN GENDER BERBASIS SUARA\r\nMENGGUNAKAN ALGORITMA HYBRID CNN-BILSTMTRANSFORMER",
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