Implementations of The Fuzzy C-Means Clustering Method in Song List Based on Emotional Mood

Songs are a popular form of entertainment worldwide, covering various types, genres, and moods. Listeners often seek playlists that match their mood. To facilitate easier navigation, this study uses the Fuzzy C-Means Clustering method to group songs based on their emotional atmosphere. By applying fuzzy logic to normalized song data, the study develops a clustering model to classify songs by mood. The resulting model achieves an FPC value of 0.56303 and a Silhouette value of 0.35861, effectively grouping songs by their emotional tone.