Music Playlist Personalization Using Clustering Analysis of Large-Scale Spotify Audio Features - Qurothu Aini Ajizah; Albaar Rubhasy (ISB Atma Luhur, 2026)
Music streaming platforms provide access to extensive song collections; however, the abundance of available content often makes it difficult for users to create playlists that remain consistent with their musical preferences. This study proposes a content-based playlist personalization approach by grouping songs using clustering analysis of Spotify audio features, including danceability, energy, valence, tempo, loudness, and acousticness. K-Means clustering is applied to identify groups of songs with similar audio characteristics, and the number of clusters is determined through a multi-criteria evaluation to ensure a balance between compactness and separation. The results indicate that a two-cluster configuration provides the most stable and interpretable structure, supported by the highest Silhouette score (0.315). The identified clusters reveal distinct musical profiles, particularly along the energy and acousticness dimensions, which can be associated with different listening contexts. These findings suggest that clustering based on audio features can support the construction of more coherent playlists by grouping songs with consistent characteristics. This study contributes by providing a structured approach to cluster selection and demonstrating its relevance for playlist personalization, especially in scenarios where user interaction data is limited.