Implementation of Supervised Learning Algorithm on Spotify Music Genre Classification
DOI:
https://doi.org/10.57152/ijatis.v2i1.1123Keywords:
Decision Tree, K-NN, Music Genre, Naïve Bayes, Random Forest, SpotifyAbstract
Spotify is a music streaming application that has been around since 2008. In the application, users can compile a playlist of songs they want to listen to. Users can determine the name of the singer, type of music, music genre and tempo of the music they want to listen to play as needed. The genre received by each user from his device will produce different recommendations, this is due to the classification process based on music listening behavior, such as songs that are often, rarely, or even never listened to or played at all by users. Therefore, the process of classifying music genres on spotify with the help of machine learning using supervised learning algorithms with algorithms namely Naïve Bayes, K-Nearest Neighbors (K-NN), Random Forest and Decision Tree with the aim of comparing the accuracy of each algorithm so as to get the best model for calcification. The results of this study obtained Random Forest has the highest accuracy value of 79.40%, followed by Decision Tree at 79.30%. In the next position Naïve Bayes with an accuracy value of 77.28%, the algorithm with the lowest accuracy is K-NN with an accuracy value of 60.74%. Meanwhile, evaluation with the t-test algorithm with the best performance is obtained from the Random Forest algorithm with a value of 0.794. It can be concluded that the best algorithm in music genre classification on Spotify is using Random Forest.
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