Optimization of the K-Means Method and Davies-Bouldin Index (DBI) Technique in Mapping Spotify's Most Popular Songs Based on Mood
Year: 2025
Type: article
Abstract: Spotify is a leading music streaming platform that offers a wide variety of songs with audio characteristics capable of influencing listeners' moods. This study aims to optimize the K-Means method to cluster popular songs based on users’ moods, with the support of the Davies-Bouldin Index (DBI) technique to determine the optimal number of clusters. The dataset was obtained from Kaggle, utilizing audio features such as danceability, valence, energy, and others as the basis for clustering. The results show that the implementation of K-Means optimized with DBI produces more accurate clustering, as indicated by lower DBI values. This approach has the potential to enhance mood-based music recommendation systems, enriching the user experience.
Authors: Rizky Septiani, Muhammad Ridwan Lubis, Fahmi Firzada
Institution: STIKOM Tunas Bangsa
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413817840