Comparation of Decision Tree Algorithm, Naive Bayes, K-Nearest Neighbords on Spotify Music Genre

Desvita Hendri (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Diana Nadha (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Faishal Khairi Basri (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Muhammad Farid Wajdi (University of Sussex, England)
Nurul Nadhirah (Islamic International University, Malaysia)

Indonesian Journal of Applied Technology and Innovation Science
Vol. 1 No. 1 (2024): IJATIS February 2024

Comparison of Decision Tree, Naive Bayes, K-Nearest Neighbords Algorithm on Spotify Music Genre Decision Tree, Naive Bayes, K-Nearest Neighbords This research aims to compare three algorithms Decision Tree, Naive Bayes and K-Nearest Neighbors (K-NN) in classifying Spotify music genres using dataset from Kaggle. The results show that the Decision Tree algorithm produces an accuracy of 23%, Naive Bayes 17%, and K-Nearest Neighbors 19%. This research provides an overview of Spotify music listeners in choosing music genres. Based on research results, the Decision Tree algorithm has the highest accuracy in classifying Spotify music genres, with the Electric Dance Music (EDM) genre being the most popular among Spotify music fans, followed by rap, pop, r&b, Latin and rock. . Meanwhile, the Naive Bayes and K-Nearest Neighbors algorithms show lower accuracy.