Analysis of Music Genre Trends on Spotify Indonesia Using the Louvain Algorithm (Top 100 Songs, May 2025)

Source abstract

Digital music streaming platforms have transformed the way audiences consume music, making Spotify one of the leading sources for analyzing music popularity and listening trends. This study investigates music genre trends in the Spotify Indonesia Top 100 dataset for May 2025 using a graph-based community detection approach. Songs were represented as nodes connected through genre similarity and normalized popularity attributes, including Streams, Total Streams, and Days on Chart. A weighted graph was constructed using Euclidean distance and inverse-distance similarity, after which communities were detected using the Louvain algorithm. The detected communities were further analyzed according to genre composition and streaming performance, while community quality was evaluated using modularity. The results identified 15 communities with a modularity value of 0.776, indicating a well-defined community structure. Although genre information was incorporated during graph construction, the detected communities were also influenced by similarities in popularity characteristics, revealing structural relationships beyond simple genre classification. Based on accumulated Total Streams, Indie Pop, Pop, and Pop Rock emerged as the dominant genres in the analyzed dataset. These findings demonstrate that graph-based community detection provides additional insights into song connectivity and genre organization that cannot be obtained through conventional descriptive aggregation alone.