Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal The Indonesian Journal of Computer Science
Dimas Elang Setyoko
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Klasifikasi Genre Music Menggunakan Hybrid Resnet18-Bidirectional LSTM dengan Mekanisme Atensi dan Fitur MFCC dan Mel-Spectogram Dimas Elang Setyoko
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4456

Abstract

Incorrect Genre Classification is still often found. One of the causes is the selection of inappropriate features. This has an impact on the ability of the classifier model because some methods with a machine learning approach are highly dependent on the features used. Utilization of several features, especially spectral features, can improve the performance of the classifier model. On the other hand, methods with a deep learning approach such as CNN and RNN have been proven to outperform machine learning-based methods. This study proposes a hybrid Resnet18-BiLSTM model with the addition of the Convolutional Block Attention Module (CBAM) attention mechanism to improve the accuracy of music genre classification. Moreover, this study also combines two spectral features, namely mel-spectrogram and MFCC. The results of the experiment using the GTZAN dataset showed that the combination of mel-spectrogram and MFCC and the addition of the CBAM attention mechanism were able to classify music genres with an accuracy rate of 95.60% in validation and 95.30% in testing.
Co-Authors