Penerapan Convolutional Neural Network Dalam Klasifikasi Genre Musik Pop Islami
DOI:
https://doi.org/10.29407/noe.v9i01.26871Keywords:
klasifikasi genre musik, CNN, fitur audio, musik Islami, Mel SpectrogramAbstract
Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi otomatis guna membedakan genre musik Islami dan Pop berdasarkan karakteristik audio. Kategori musik Islami dalam konteks ini merujuk pada lagu-lagu yang secara nuansa dan aransemen mencerminkan nilai-nilai religius, tanpa melakukan analisis terhadap lirik. Data dikumpulkan dari platform Spotify dan melalui tahap pra-pemrosesan berupa augmentasi audio untuk memperkaya variasi data. Proses selanjutnya adalah ekstraksi fitur menggunakan MFCC, Chroma, dan Mel Spectrogram, yang kemudian digunakan sebagai masukan dalam pelatihan model Convolutional Neural Network (CNN). Model CNN yang dibangun berhasil mencapai akurasi sebesar 74% pada data pengujian, dengan nilai precision, recall, dan f1-score yang menunjukkan performa cukup seimbang, terutama dalam mengenali pola khas dari music Pop Islami. Hasil ini menunjukkan bahwa pendekatan berbasis fitur audio mampu mengenali genre musik secara efektif, meskipun masih terdapat tantangan dalam menangani variasi kompleks dari genre Pop. Sebagai bentuk implementasi, sistem ini dikembangkan dalam bentuk aplikasi antarmuka sederhana yang memungkinkan pengguna mengunggah file audio dan mendapatkan hasil prediksi secara langsung.Downloads
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