Music Genre Classification Using Random Forest Model - Simarmata, Ivan Luis; Supriana, I Wayan (JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), 2023)
Music genre is a grouping of music based on their style. To group music into certain genres is a long and boring task to do manually because one must listen to each song individually and determine which genre does this song belong to. This process can be made automatic using classification models like Random Forest. The Random Forest model is a mutated version of the decision tree model, where Random Forest uses multiple decision trees to get a single result. In this paper the model that will be tested is the Random Forest model and XGB Classification model for comparison. The XGB Classification model is used to compare because it is similar to the Random Forest model. XGB Classification is a mutated decision tree model which uses CART as its tree. The results show that with the Random Forest model, an accuracy of 72% is achieved when all audio features are included, and with the XGB Classification, an accuracy of 73% is achieved with some audio features dropped.