Analisis Klaster Lagu Indonesia pada Platform Spotify Menggunakan Algoritma K-Means (Studi Kasus: Spotify)

Rizki Nur Azim; Sudin Saepudin

doi:10.70340/jirsi.v5i2.417

Authors

Rizki Nur Azim Universitas Nusa Putra
Sudin Saepudin Universitas Nusa Putra

DOI:

https://doi.org/10.70340/jirsi.v5i2.417

Keywords:

Clustering Algorithm, Data Mining, Indonesian Music, K-Means, Spotify Chart

Abstract

The growth of streaming platforms like Spotify has revolutionized music consumption in Indonesia, generating complex song performance data from Spotify Charts. This study aims to cluster Indonesian songs by popularity using the K-Means Clustering algorithm. The selected variables—peak, previous, streak, and streams—were chosen because they directly reflect song performance dynamics on the charts, encompassing best position, rank movement, longevity, and play volume, making them more representative of market resonance than audio features alone. A quantitative descriptive approach was applied to Indonesia Spotify Charts data spanning January 2020 to December 2024, with a purposive sample of 5,316 records post-cleaning. The optimal number of clusters was determined via the Elbow Method at k=3, with cluster quality evaluated using a Silhouette Score of 0.4968. Findings reveal three clusters: Cluster 0 (medium popularity, 3.8M average streams), Cluster 1 (low popularity, 569K average streams), and Cluster 2 (high popularity, 54M average streams). The study concludes that K-Means effectively identifies objective popularity patterns to support data-driven promotion strategies.

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