Analisis dan Klasifikasi Genre Musik Menggunakan Algoritma STFT dan Random Forest - Manalu, Merry Royanti; Raharja, Made Agung (Jurnal Nasional Teknologi Informasi dan Aplikasnya; Vol 3 No 1 (2024): JNATIA Vol. 3, No. 1, November 2024DO - 10.24843/JNATIA.2024.v03.i01.p24, 2024)
This research analyzes the classification of music genres using the Short Time Fourier Transform (STFT) algorithm. The main objective is to identify the effectiveness of STFT, along with the Random Forest classification algorithm, in distinguishing music genres based on their spectral characteristics. The STFT method is utilized to transform audio signals into a spectral representation within a short time window. The extracted spectral features are then fed into the Random Forest classification algorithm to classify different music genres. This research involves the use of representative datasets from various music genres for performance evaluation. Experimental results show that using STFT as a feature and employing the Random Forest classification algorithm in the process are able to provide satisfactory results in distinguishing music genres, with an accuracy of 86%. These findings demonstrate the potential of STFT, in combination with Random Forest, as a useful tool in music analysis and automatic classification of music genres.