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All Journal Journal of Information Technology (JIfoTech) FRIMA: Festival Riset Ilmiah Manajemen & Akuntansi KREATIF: Jurnal Pengabdian Masyarakat Nusantara SisInfo : Jurnal Sistem Informasi dan Informatika KREATIF: Jurnal Pengabdian Masyarakat Nusantara Bulletin of Intelligent Machines and Algorithms
Darsiti .
Universitas Teknologi Digital
Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : bulletin of intelligent machines and algorithms

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A Comprehensive Machine Learning Approach for Predicting Beats Per Minute (BPM) in Music Using Audio Features Darsiti Darsiti
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 1 (2025): BIMA November 2025 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i1.2

Abstract

Predicting Beats Per Minute (BPM) in music is a significant challenge due to the complexity of the relationship between various audio features, such as rhythm, energy, and mood. Traditional methods are often unable to handle the complexity of feature variations and interactions. This study aims to develop a more accurate and reliable machine learning model to predict song BPM based on extracted audio features. We use advanced machine learning algorithms, including LightGBM, XGBoost, and Random Forest, to train models with a dataset covering ten audio features. Evaluation is performed using a k-fold cross-validation scheme with RMSE, MAE, and R² Score metrics. The experimental results show that boosting-based models such as LightGBM produce the best performance, with the lowest RMSE of 10.48, the lowest MAE of 7.62, and the highest R² Score of 0.83. However, these models still show a tendency to regress to the mean, indicating that some more extreme BPM variations are not fully captured. These findings emphasize the importance of improvements in feature engineering techniques and data rebalancing to improve BPM prediction accuracy in practical applications, such as music recommendation systems and tempo analysis.
Co-Authors Asep Mahmudin Budiman Cahya Putri Julyandaru Fadlah Aulia Putri Meilani Gustian  Salsa Sabila