Klasifikasi Jenis Audio Berdasarkan Kondisi Psikologi Menggunakan Kombinasi Algoritme Self Organizing Maps dan Learning Vector Quantization - Rayhan Tsani Putra; Imam Cholissodin; Candra Dewi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
The characteristics of each type of audio have different effects on human emotions as well as what activities are being performed. The most common case in most societies is listening to music that has been commonly heard without caring about the right conditions. It would be better if you can maximize the positive impact of the audio. Classification of audio types will be very helpful in determining the appropriate audio type. This study classifies the type of audio based on one of the psychological conditions of emotion and also some types of activities using a combination of SOM-LVQ algorithms (Self Organizing Map and Learning Vector Quantization). SOM is used as an algorithm that accompanies and trains initial weights for LVQ because it has a structure and workflow similar to LVQ. Feature used in this research is 11 which consist of psychology condition and activity type. There are 4 types of audio that became the class in this study. The maximum accuracy obtained in this study was 89.583%. The SOM-LVQ algorithm combination achieves the maximum accuracy with 4 training iterations, while LVQ requires 6 iterations to achieve maximum value. Although with the same accuracy, SOM-LVQ is faster to get the optimal value.