Pencirian Isyarat Data Muzik yang Terhasil dari Alat Muzik Tradisional Menggunakan Ciri Fraktal

Abstract

Musical instruments are usually distinguished by their sound produced through human perception which may lead to misinterpretation due to auditory perception bias and other disturbances. Therefore, recognition using music signals is carried out to help characterize signals from different musical instruments. Fractal analysis is a mathematical tool used to study complex and irregular patterns in various systems. In this study, fractal analysis was used to study and analyze musical notes signal data from different instruments. The fractal analysis method used is the box counting method. The traditional musical instruments involved in this study are the seruling, cak lempong, kompang, and gambus. Matlab software was used to analyze the musical data signal. First, the fractal dimension of the music data signal in the form of time domain is calculated. Then, the mean and standard deviation of the fractal dimension values were determined to recognize different musical instruments. Additionally, different image resolutions and box sizes are also used to calculate the fractal dimension of musical instrument from the time domain data. The error of best fit line, E will also be calculated to ensure the reliability of box counting dimensions using leastsquares regression method. The results show that the value of the fractal dimension for all 18 data is between 1.2636 to 1.7543. In terms of musical instrument recognition, the recognition of seruling and kompang using mean fractal dimension is successful. However, this method is less suitable for identification for cak lempong and gambus due to the high standard deviation. By using different image resolutions and box sizes, the accuracy of fractal dimension values will be affected. Increasing these parameters increases the accuracy of the results. The results of the study show that an image with a resolution of 1024 x 1024 pixels and a scaling factor of 9 is suitable to analyze the fractal characteristics of musical instrument data signals by using the box counting method.