Klasterisasi Lagu Populer dan Eksplorasi Subgenre Spotify 2024 dengan K-Medoids
DOI:
https://doi.org/10.55606/jitek.v5i1.5699Keywords:
K-Medoids, Spotify, genre, clustering, subgenreAbstract
Spotify's genre classification system remains too broad, often grouping songs with distinct characteristics into the same category. For example, Pop Ballads and Dance Pop are frequently classified under "Pop" despite significant differences in tempo, emotion, and production style. This leads to inaccurate song recommendations. This study applies the K-Medoids algorithm to enhance song classification based on Spotify Playlist Count, Spotify Playlist Reach, and Spotify Popularity. The CRISP- DM methodology guides business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Clustering results without popularity ranking reveal three main groups: songs with low playlist count but high reach (dominated by light hip-hop), songs with high playlist count and reach (dominated by contemporary R&B), and songs with low popularity (dominated by dance). After ranking by popularity, clusters became more defined, with alternative pop dominating the high-reach cluster, contemporary R&B in the popular cluster, and dance pop in the less popular cluster. Evaluation using a Silhouette Score of 0.5014 indicates good cluster quality. Additionally, this study successfully identified the 15 most popular songs on Spotify in 2024. These findings can help Spotify refine its recommendation system by incorporating subgenre-based classification, ensuring more accurate search results aligned with user preferences and evolving music trends.
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