Analisis Klaster dan Klasifikasi Emosi Dalam Musik K-Pop dengan K-Means dan Algoritma C 4.5

The main objectives are to identify emotion patterns hidden in K-Pop music based on audio features extracted from the Spotify API and to build an emotion classification model that can predict the emotions of K-Pop songs.In this approach, the K-Means algorithm is used to cluster K-Pop songs based on audio features such as energy, valence, tempo, danceability, and speechiness. The clustering results reveal several main groups that represent variations in musical characteristics and emotions. Next, the C4.5 algorithm was used to build an emotion classification model based on the clustering results. The C4.5 model showed high performance with accuracy reaching 99.48% on a 90:10 dataset split, 99.21% on an 80:20 split, and 98.95% on a 70:30 split.The Streamlit application was developed to visualize emotion predictions from K-Pop songs with a web-based user interface. In addition, Ngrok was used to provide remote access to this application, allowing users to test and use the application remotely.The results of this study show that the combination of K-Means and C4.5 can effectively cluster and classify emotions in K-Pop music, providing valuable insights into the musical characteristics that influence emotions. This application has the potential to be used in further analysis, development of intelligent features in music applications, and improvement of user experience in listening to K-Pop music.