Clustering Analysis of MAMA 2024 Song of The Year Nominees Based on Musical Elements and Popularity Indicators
DOI:
https://doi.org/10.21512/comtech.v16i2.12860Keywords:
hierarchical clustering, musical elements, popularity indicators, MAMA 2024, song analysisAbstract
As K-pop continues to dominate global music charts, understanding the factors behind the success of songs has become increasingly essential. This study explores how musical elements and popularity indicators reveal patterns among top-performing songs. A total of 57 songs nominated for the 2024 Song of the Year category were grouped using hierarchical cluster analysis. The genre variable was consolidated into six broader categories and converted into numerical labels. All variables were normalized using the Min-Max normalization method before clustering. The data included musical elements such as genre, tempo, danceability, energy, and happiness, as well as popularity indicators like YouTube views and Spotify streams. The analysis employed single, complete, and average linkage methods. Among these, the average linkage method yielded the best results, with an agglomerative coefficient value of 0.8167. Seven distinct clusters were identified: Cluster 1 featured R&B and hip-hop styles with varied energy and rhythms; Cluster 2, the largest group, included high-energy pop, hip-hop, and dance-pop tracks that are popular on streaming platforms; Cluster 3 contained indie and experimental tracks; Cluster 4 emphasized high-energy stage performances; Cluster 5 was an outlier with experimental traits; Cluster 6 highlighted R&B and funk with global appeal; and Cluster 7 included emotional OSTs and ballads with slower tempos. By combining musical elements and popularity indicators, this study uncovers patterns of success in K-pop songs. These findings offer actionable insights for artists, producers, and marketers, providing a data-driven reference for creating music that resonates with modern audience preferences.
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