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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Conference SENATIK STT Adisutjipto Yogyakarta Compiler ComTech: Computer, Mathematics and Engineering Applications
Elizabeth Nurmiyati Tamatjita
Sekolah Tinggi Teknologi Adisutjipto
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering

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Journal : IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

 1 
Klasifikasi Lagu Berdasarkan Genre pada Format WAV Nurmiyati Tamatjita; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 2 (2014): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.6542

Abstract

AbstrakDalam dunia yang berkembang pesat, media audio semakin komplek. Karena itulah diperlukan sebuah mekanisme penentuan jenis lagu (genre) yang tepat secara efektif dan efisien.  Pencarian secara manual sudah tidak efektif dan efisien lagi karena banyaknya data yang tersimpan.          Zero Crossing Rate (ZCR), Average Energy (E) dan Silent Ratio (SR) adalah 3 Feature Extraction yang digunakan untuk klasifikasi pencarian 12 genre.Tiga dimensi adalah bentuk visualisasi pengukuran tingkat kemiripan sebuah data berdasarkan hasil klasifikasi yang diinput oleh user.            Dalam penelitian ini pengujian klasifikasi menggunakan metode 3, 6, 9 dan 12 genre melalui jarak terdekat (Euclidean Distance). Hasil pengujian yaitu menunjukkan bahwa 3 genre yaitu Balada, Blues dan Classic menunjukkan = 96,67%, 6 genre yaitu Balada, Blues, Classic, Harmony, Hip Jop dan Jazz menunjukkan = 70% dan 9 genre yaitu Balada, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin dan Pop menunjukkan = 53,33% serta 12 genre = 33,33% Kata Kunci— Zero Crossing Rate (ZCR), Average Energy (E), Silent Ratio (SR), Euclidean Distance  Abstract            Music genre is getting complex from time to time. As the size of digital media grows along with amount of data, manual search of digital audio files according to its genre is considered impractical and inefficient; therefore a classification mechanism is needed to improve searching.            Zero Crossing Rate (ZCR), Average Energy (E) and Silent Ratio (SR) are a few of features that can be extracted from digital audio files to classify its genre. This research conducted to classify digital audio (songs) into 12 genres: Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop, Electronic, Reggae and Rock using above mentioned features, extracted from WAV audio files. Classification is performed several times using selected 3, 6, 9 and 12 genres respectively.            The result shows that classification of 3 music genres (Ballad, Blues, Classic) has the highest accuracy (96.67%), followed by 6 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz) with 70%, and 9 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop) with 53.33% accuracy. Classification of all 12 music genres yields the lowest accuracy of 33.33%.   Keywords— Zero Crossing Rate (ZCR), Average Energy (E), Silent Ratio (SR), Euclidean Distance
Co-Authors Ade Ari Ardianto Aditya Wikan Mahastama Agus Harjoko Anggraini Kusumaningrum Anggraini Kusumaningrum, Anggraini Angraini Kusumaningrum Ardianto, Ade Ari Bagus Pratama, Endyatna Puthut Bere, Gracelia Adelaida Brama Ady Sulistiyo Deni Fajar Pratama Dewi, Wahyu Sofyanti Dwi Nugraheny Dwi Nugraheny, Dwi Endyatna Puthut Bagus Pratama Gracelia Adelaida Bere Guruh Marindra Pratama Haruno Sajati Irawaty, Mardiana Kusumaningrum, Angraini Pratama, Deni Fajar Pratama, Guruh Marindra Ramdhani, Rio Rio Ramdhani Sulistiyo, Brama Ady Sumarsono Sumarsono Sumarsono Sumarsono Wahyu Sofyanti Dewi Yuliani Indrianingsih Yuliani Indrianingsih