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All Journal Jurnal Nasional Teknologi Informasi dan Aplikasinya
Maria Dorteah Rumpumbo
Universitas Udayana
Computer Science & IT Engineering

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Optimasi Model Gaussian Mixture Model (GMM) untuk Klasifikasi Genre Musik Berbasis Mel-Frequency Cepstral Coefficients (MFCC) Maria Dorteah Rumpumbo; I Made Widhi Wirawan
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p17

Abstract

Music genre classification is an increasingly relevant field as the number of digital music collections increases. The main challenge in this classification is to effectively capture the acoustic characteristics of different genres. This research proposes an optimization of the Gaussian Mixture Model (GMM) model to improve the accuracy of music genre classification using the Mel-Frequency Cepstral Coefficients (MFCC) feature. The dataset used covers various genres such as rock, classical, and jazz. The feature extraction process is carried out through MFCC and continued by training the GMM model with an optimized number of components. The test results show that the combination of MFCC and optimized GMM is able to improve the classification performance compared to conventional approaches. This study contributes to the development of an efficient machine learning-based music classification system.
Co-Authors I Made Widhi Wirawan