Analisis Kelayakan Penerapan Machine Learning pada Industri Musik

Farid Hasfindra; Rahmad Gunawan

doi:10.33365/jtikom.v7i1.1746

Farid Hasfindra Universitas Muhammadiyah Riau
Rahmad Gunawan Universitas Muhammadiyah Riau

DOI: https://doi.org/10.33365/jtikom.v7i1.1746

Abstrak

The application of machine learning (ML) has transformed the music industry, offering efficiencies in music generation, genre classification, vocal training, and music education. However, this vast potential is countered by fundamental challenges, including dataset bias, legal issues surrounding Artificial Intelligence (AI) copyright, ethical challenges related to algorithmic transparency, and accessibility barriers for independent musicians. This literature review aims to assess the feasibility of ML solutions in the music industry through a comprehensive analysis of technical, legal, ethical, and economic aspects. The method used is a Systematic Literature Review (SLR) guided by the PRISMA framework, involving a thematic analysis of 29 scientific articles that passed the selection and quality assessment process. The results demonstrate that ML is proven efficient in improving the accuracy of music generation (83-90%), genre classification performance, and personalized vocal learning experiences. Nevertheless, full feasibility is hindered by data bias issues (the dominance of Western music datasets), ambiguity in copyright regulations for AI generated music, computational costs, and a lack of model transparency. In conclusion, current ML implementation is feasible within specific contexts, such as educational institutions and large platforms with adequate infrastructure. Future prospects require the development of more representative datasets, transparent models, and clear regulatory frameworks to address industry inequities.

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