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All Journal Jurnal Nasional Teknologi Informasi dan Aplikasinya
Theresia Margaretha Purba
Universitas Udayana
Computer Science & IT Engineering

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Klasifikasi Instrumen Musik Menggunakan Metode Machine Learning Theresia Margaretha Purba; I Gusti Agung Gede Arya Kadyanan
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.p24

Abstract

Music plays an important role in human life, and automatic identification of musical instruments is becoming an increasingly relevant field in the digital era. This study aims to classify musical instrument types based on acoustic features using machine learning methods, specifically Support Vector Machine (SVM). The dataset used contains audio recordings of four instruments, namely guitar, piano, drum, and violin. Each audio file goes through a preprocessing process such as sample rate standardization, duration trimming, and framing. Furthermore, feature extraction is carried out from the time domain (Zero Crossing Rate and RMS), frequency domain (Spectral Centroid, Spread, and Roll-off), and cepstral domain (MFCC). The SVM model is trained with a combination of various features and evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results show that the combination of all features produces the best accuracy of 68.33%. Although its performance is not optimal, these results show the potential of a feature-based approach for musical instrument classification and become the basis for further development using more complex methods such as deep learning.
Co-Authors I Gusti Agung Gede Arya Kadyanan