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      "abstract": "Musik merupakan media ekspresi yang memiliki kemampuan luar biasa\r\ndalam menyampaikan emosi dan memengaruhi suasana hati pendengarnya. Namun,\r\nklasifikasi mood musik secara otomatis menghadapi tantangan yang signifikan\r\nkarena sifat audio yang kompleks, multidimensional, dan subjektif. Sistem\r\nklasifikasi manual yang mengandalkan penilaian manusia dinilai tidak efisien dan\r\ntidak skalabel untuk mengelola koleksi musik dalam jumlah besar, seperti pada\r\nplatform streaming modern. Ketidakmampuan sistem otomatis untuk mengenali\r\nnuansa emosional dalam musik berdampak pada pengalaman pengguna yang\r\nkurang personal, rekomendasi lagu yang tidak akurat, serta keterbatasan dalam\r\npengembangan aplikasi berbasis konteks emosional seperti terapi musik, kurasi\r\nplaylist adaptif, dan sistem rekomendasi cerdas.\r\nPenelitian ini mengembangkan sistem klasifikasi mood musik berbasis\r\nsinyal suara menggunakan algoritma Convolutional Neural Network (CNN) untuk\r\nlima kelas mood, yaitu aggressive, dramatic, romantic, sad, dan happy. Dataset\r\nyang digunakan adalah \"Music Classification\" dari Kaggle, terdiri dari 10.133\r\nsampel audio berformat WAV dengan durasi 10 detik dan sampling rate 22.050 Hz.\r\nFitur audio diekstraksi melalui empat channel, yakni MFCC, Delta MFCC, DeltaDelta\r\nMFCC,\r\ndan\r\nMel-Spectrogram,\r\nyang\r\nkemudian\r\ndigabungkan\r\nmenjadi\r\ntensor\r\n\r\nberukuran\r\n(40\r\n\r\n× 130 × 4) sebagai input model. Arsitektur CNN yang dirancang\r\nterdiri dari empat blok konvolusi, Batch Normalization, GlobalAveragePooling2D,\r\nserta dua lapisan fully connected yang dilengkapi regularisasi dropout dan L2. Guna\r\nmenangani ketidakseimbangan kelas dalam dataset, diterapkan balanced class\r\nweight selama proses pelatihan model.\r\nModel terbaik yang dihasilkan mencapai akurasi sebesar 79,93% dan\r\nprecision 80,88% pada data uji, menunjukkan kemampuan generalisasi yang baik\r\nterhadap data yang belum pernah dilihat sebelumnya. Penerapan balanced class\r\nweight terbukti berkontribusi signifikan dalam meningkatkan performa model\r\nsecara keseluruhan. Sistem kemudian di-deploy menggunakan framework Flask\r\nsebagai REST API dengan antarmuka web interaktif, sehingga memungkinkan\r\nprediksi mood musik secara langsung dan mudah diakses. Hasil penelitian ini dapat\r\ndimanfaatkan oleh pengembang aplikasi musik, platform streaming, peneliti di\r\nbidang Music Information Retrieval (MIR), serta praktisi terapi musik berbasis\r\nteknologi. Untuk penelitian lebih lanjut, disarankan eksplorasi arsitektur yang lebih\r\ndalam seperti ResNet atau penggunaan model transformer berbasis audio seperti\r\nAST (Audio Spectrogram Transformer), perluasan kelas mood, serta pemanfaatan\r\nteknik augmentasi data audio guna meningkatkan akurasi dan ketahanan model\r\npada kondisi audio dunia nyata yang lebih beragam.",
      "date_type": "published",
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      "keywords": "Klasifikasi Mood Musik, MFCC, Mel-Spectrogram, Convolutional\r\nNeural Network, Class Weight, Music Mood Classification.",
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