Penerapan Algoritma Random Forest dalam Prediksi Emosi Musik Berdasarkan Karakteristik Fitur Audio Spotify
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Abstract
Emotion classification in music is a crucial aspect in developing context-aware recommendation systems that respond to the listener’s mood. The Random Forest algorithm is used to map song emotions based on Spotify audio features, namely valence, energy, loudness, and danceability, which reflect the psychological and acoustic aspects of music. The dataset was collected through the Spotify Web API and public repositories, consisting of songs released between 2009 and 2019. Data processing involved normalization and labeling emotions into five categories: Angry, Calm, Happy, Neutral, and Sad. The model was trained using 70% of the data and tested with the remaining 30%. Evaluation results showed an accuracy of 98.75%, with perfect F1-scores for the Happy and Sad categories. Valence and energy were found to be the most influential features, while Calm was often confused with Neutral due to similar acoustic patterns. These findings demonstrate that the Random Forest approach is effective in accurately and consistently classifying music emotions based on audio features.
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