CLUSTERING POP SONGS BASED ON SPOTIFY DATA USING K-MEANS AND K-MEDOIDS ALGORITHM - Novia Ayu Privandhani; Sulastri (Institute of Computer Science (IOCS), 2022)
The development of music service technology is currently making it easier to listen to songs. One of them is the Spotify application. Services on music attributes are random, such as Danceability, Energy, Acousticness, Instrumentalness, Liveness, Loudness, Speechiness, Valence, and Tempo. The purpose of this study is to compare the results of clustering on the K-Means and K-Medoids algorithms using the Rstudio tools. The results of this comparison obtain the optimal number of clusters and obtain high, medium, and low cluster results. The results of the K-Means calculation are based on the average in cluster 1 the highest is Tempo with a value of 118 in cluster 2 the highest is Tempo with a value of 125, and in cluster 3 the highest is Tempo with a value of 123. The calculation of K-Medoids is based on the average in cluster 1 the highest is Tempo with a value of 129, in cluster 2 the highest is Tempo with a value of 122, and in cluster 3 the highest is Tempo with a value of 110.