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Arizka, Puput Nur
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Computer Science & IT Engineering

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Comparison of Random Forest and Naive Bayes Algorithms in Classification of Song Popularity on the Spotify Platform Saputro, Janu Ilham; Fantomi, Rian; Simbolon, Saoloan; Arizka, Puput Nur; Ramadani, Berlina
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37578

Abstract

The purpose of this study is to use machine learning to rank Spotify songs based on how popular they are. Because there is so much music data out there, musicians and artists need to know if a song will be popular or not. The dataset has 8,778 songs, each with different features like how popular the artist is, how many followers they have, and other song details. This research evaluates the efficacy of two classification algorithms: Random Forest and Naive Bayes. Artist popularity, artist followers, explicit album total tracks, and track number are the main things that are used to make models. The results of the experiment show that the Random Forest algorithm works better than the Naive Bayes algorithm. The Random Forest algorithm was right 76.54% of the time, but the Naive Bayes algorithm was only right 72.21% of the time. The f1-score for both popularity classes is also better for Random Forest. This finding shows that ensemble-based models, like Random Forest, work better with the features of music popularity data than basic probabilistic models do.
Co-Authors Fantomi, Rian Ramadani, Berlina Saputro, Janu Ilham Simbolon, Saoloan