Klasifikasi Alat Musik Tradisional dengan Metode Machine Learning dengan Librosa dan Tensorflow pada Python
(1) Jurusan Teknik Elektro Program Studi Teknik Telekomunikasi, Politeknik Negeri Sriwijaya Palembang
(2) Jurusan Teknik Elektro Program Studi Teknik Telekomunikasi, Politeknik Negeri Sriwijaya Palembang
(3) Jurusan Teknik Elektro Program Studi Teknik Telekomunikasi, Politeknik Negeri Sriwijaya Palembang
(*) Corresponding Author
Abstract
Full Text:
PDFReferences
IBM Cloud Education. 2020. Machine Learning, https://www.ibm.com/cloud/learn/machine-learning.
Tensorflow, https://www.tensorflow.org/.
GitHub, Inc. 2021. Tensorflow, https://github.com/tensorflow/tensorflow.
Mel Frequency Cepstral Coefficient (MFCC) tutorial, http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/.
Librosa Development Team. Librosa. 2013-2021, https://librosa.org/doc/main/index.html.
Hidden Layer Perceptron In Tensorflow, https://www.javapoint.com/hidden-layer-perceptron-in-tensorflow.
Arik Dian Pratama, Hozairi, & Tony Yulianto. Klasifikasi Musik Berdasarkan Genre Menggunakan Jaringan Syaraf Tiruan. Vol 2, No 1. Oktober 2016.
Danny Lionel, Rudy Adipranata, & Endang Setyati. Klasifikasi Genre Musik Menggunakan Metode Deep Learning Convolutional Neural Network dan Mel-Spektrogram. Vol 7, No 1. 2019.
Huzaifah, M. 2017. Comparison of Time-Frequency Resperesentations for Environmental Sound Classification using Convolutional Neural Network. arXiv: 1706.07156.
Endang Retnoningsih, Rully Pramudita. Mengenal Machine Learning Dengan Teknik Supervised dan Unsupervised Learning Menggunakan Python. Vol 7, No.2, Desember 2020. 156-165
DOI: http://dx.doi.org/10.30645/j-sakti.v5i2.390
Refbacks
- There are currently no refbacks.
J-SAKTI (Jurnal Sains Komputer & Informatika)
Published Papers Indexed/Abstracted By:
Jumlah Kunjungan :