Authors:

Ivan Luis Simarmata, I Wayan Supriana

Abstract:

“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.”

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PDF:

https://jurnal.harianregional.com/jlk/full-92614

Published

2023-01-26

How To Cite

SIMARMATA, Ivan Luis; SUPRIANA, I Wayan. Music Genre Classification Using Random Forest Model.JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 12, n. 1, p. 83-88, jan. 2023. ISSN 2654-5101. Available at: https://ojs.unud.ac.id/index.php/JLK/article/view/92614. Date accessed: 08 Jul. 2024. doi:https://doi.org/10.24843/JLK.2023.v12.i01.p10.

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ABNT, APA, BibTeX, CBE, EndNote - EndNote format (Macintosh & Windows), MLA, ProCite - RIS format (Macintosh & Windows), RefWorks, Reference Manager - RIS format (Windows only), Turabian

Issue

Vol 12 No 1 (2023): JELIKU Volume 12 No 1, August 2023

Section

Articles

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License