CLUSTERING OF POPULAR SPOTIFY SONGS IN 2023 USING K-MEANS METHOD AND SILHOUETTE COEFFICIENT

  • Nur Rohman (1*) Program Studi Ilmu Komputer Fakultas Teknologi Informasi Universitas Budi Luhur
  • Arief Wibowo (2) Universitas Budi Luhur

  • (*) Corresponding Author
Keywords: clustering, data mining, k-means, silhouette coefficient, spotify

Abstract

The rapid advancement of technology and globalization in this era has brought about comprehensive and easily accessible music streaming services, one of which is Spotify. According to Kompas.com, Spotify has experienced a rise in subscribers up to 130 million, as a platform that offers various features besides music streaming. Spotify also provides a better user experience and has the ability to compete with other music streaming platforms. The mission of this research is to classify popular Spotify song data in 2023, which can aid in a deeper understanding of listener preferences or music trends. Based on the test results, there were 2 clusters obtained with cluster 0 containing 863 data and cluster 1 containing 90 data. From the testing results conducted in the K-Means analysis, a Silhouette Coefficient of 0.81 was obtained, which falls into the category of Strong Structure. From these results, it can be suggested that cluster formation was done very well to provide more personalized and relevant music recommendations to Spotify platform users. By understanding the preferences and patterns of listeners revealed through clustering, streaming services can enhance user experience by providing more tailored content.

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References

Aji, N. S., Natsir, F., & Istianah, S. (2023). Penentuan Penjualan Barang Berdasarkan Pengelompokan Produk dengan K-Means Clustering Metode CRISP-DM.

Asyraf, H., & Prasetya, E. (2023). Implementasi Metode CRISP DM dan Algoritma Decision Tree Untuk Strategi Produksi Kerajinan Tangan pada UMKM A. Jurnal Media Informatika Budidarma, 8, 94–105. https://doi.org/10.30865/mib.v8i1.7050

Dhewayani, F. N., Amelia, D., Alifah, D. N., Sari, B. N., & Jajuli, M. (2022). Implementasi K-Means Clustering untuk Pengelompokkan Daerah Rawan Bencana Kebakaran Menggunakan Model CRISP-DM. Jurnal Teknologi Dan Informasi, 12. https://doi.org/10.34010/jati.v12i1

Fahmi, R. N., Jajuli, M., & Sulistiyowati, N. (2021). Analisa Pemetaan Tingkat Kriminalitas Di Kabupaten Karawang Menggunakan Algoritma K-Means. Journal of Information Technology and Computer Science (INTECOMS), 4(1). Retrieved from www.pasundanekspres.co

Fransiska, N., Anggraeni, D., & Enri, U. (2022). Pengelompokkan Data Kemiskinan Provinsi Jawa Barat Menggunakan Algoritma K-Means dengan Silhouette Coefficient. Jurnal Teknologi Informasi Komunikasi, 9, 29–35. https://doi.org/10.38204/tematik.v9i1.921

Hasyim, F., & Muafi. (2022). Implementasi Data Mining Dalam Menentukan Strategi Promosi Program KB Menggunakan Algoritma K-Means Clustering. 3(1). Retrieved from https://ejournal.unuja.ac.id/index.php/core

Musyarofah, U. L., Alima, S. N., & Kartika, D. S. Y. (2022, September). KLASIFIKASI TOP 50 SPOTIFY TAHUN 2010-2019 MENGGUNAKAN METODE K-MEANS CLUSTERING. In Prosiding Seminar Nasional Teknologi dan Sistem Informasi (Vol. 2, No. 1, pp. 215-220).

Nasari, F., & Am, A. N. (2023). Implementasi K-Medoids Clustering Dalam Pengelompokkan Harga 8 Jenis Minyak Goreng. SINTECH JOURNAL, 6. Retrieved from https://doi.org/10.31598

Navisa, S., Hakim, L., & Nabilah, A. (2021). Komparasi Algoritma Klasifikasi Genre Musik pada Spotify Menggunakan CRISP-DM. In Jurnal Sistem Cerdas.

Nisa, C., & Yustanti, W. (2021). Studi Perbandingan Algoritma Klastering Dalam Pengelompokan Persediaan Produk (Studi Kasus : Subdirektorat Perencanaan Sarana Prasarana Dan Logistik PTN X). JEISBI, 02.

Privandhani, N. A. (2022). Clustering Pop Songs Based On Spotify Data Using K-Means And K-Medoids Algorithm. Jurnal Mantik, 6(2), 1542-1550.

Ramadhani, D. I., Damayanti, O., Thaushiyah, O., & Kadafi, A. R. (2022). Penerapan Metode K-Means Untuk Clustering Desa Rawan Bencana Berdasarkan Data Kejadian Terjadinya Bencana Alam. JURIKOM (Jurnal Riset Komputer), 9(3), 749. https://doi.org/10.30865/jurikom.v9i3.4326

Pratama, E. F. A., Khairil, K., & Jumadi, J. (2022). Implementasi Metode K-Means Clustering Pada Segmentasi Citra Digital. Jurnal Media Infotama, 18(2), 291-301.

Triyandana, G., Putri, L. A., & Umaidah, Y. (2022). Penerapan Data Mining Pengelompokan Menu Makanan dan Minuman Berdasarkan Tingkat Penjualan Menggunakan Metode K-Means. In Journal of Applied Informatics and Computing (JAIC) (Vol. 6). Retrieved from http://jurnal.polibatam.ac.id/index.php/JAIC

Wahyudi, T., Sa’adah, N., & Puspitasari, D. (2023). Penerapan Metode K-Means Pada Data Penjualan Untuk Mendapatkan Produk Terlaris di PT. Titian Nusantara Boga. Jurnal Sains Dan Teknologi, 5(1), 228–236. https://doi.org/10.55338/saintek.v5i1.1379

Published
2024-03-28
How to Cite
Rohman, N., & Wibowo, A. (2024). CLUSTERING OF POPULAR SPOTIFY SONGS IN 2023 USING K-MEANS METHOD AND SILHOUETTE COEFFICIENT. Jurnal Pilar Nusa Mandiri, 20(1), 18-24. https://doi.org/10.33480/pilar.v20i1.4937
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