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All Journal Jurnas Nasional Teknologi dan Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Telematika JIEMS (Journal of Industrial Engineering and Management Systems) JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Tekno Kompak Zonasi: Jurnal Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) INFORMASI (Jurnal Informatika dan Sistem Informasi) Idealis : Indonesia Journal Information System Journal of Business and Audit Information System (JBASE) OISAA Journal of Indonesia Emas Jurnal Pengabdian dan Kewirausahaan Computer Science (CO-SCIENCE) JURNAL ILMIAH GLOBAL EDUCATION Jurnal Sistem Informasi Triguna Dharma (JURSI TGD)
Bhustomy Hakim
Universitas Bunda Mulia
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Idealis : Indonesia Journal Information System

 1 
IMPLEMENTASI MACHINE LEARNING DALAM PENGELOMPOKAN MUSIK MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING Hakim, Bhustomy; Kaunang, Fergie Joanda; Susanto, Cornelius; Salim, Jonathan; Indradjaja, Reynaldi
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3357

Abstract

Music is an inseparable part of everyone's life. Many people listen to music but with different preferences because there are so many different types of music available. Many music streaming platforms compete to make song recommendations that suit their users' preferences but it is still difficult to group the music in them. This study aims to analyze music using the K-Means Clustering algorithm, an unsupervised machine learning method, to group songs based on their features such as tempo, tone, and other elements. This research was conducted in the context of the rapidly growing digitalization of music, where music streaming platforms are increasingly popular and allow for personalization of user preferences. The K-Means algorithm is used to find patterns from various music genres, so that it can provide insight into music trends and listener preferences. This study involves several main stages, including data exploration (Exploratory Data Analysis/EDA), checking for missing values ​​and outliers, and selecting relevant features. Furthermore, the clustering process is carried out using the K-Means algorithm with evaluation through the Elbow and Silhouette methods to determine the optimal number of clusters and assess the quality of clustering. This research is expected to contribute to the development of a better music recommendation system by increasing knowledge in the field of machine learning, especially in the application of the K-Means algorithm for music data clustering.
Co-Authors ., Ranny Akhmat Fauzi Anak Agung Ayu Putri Ardyanti Asvian Sulaeman Carlos Hia, Juan Chandra, Michelle Mei Diana Chang, Jennefer Febiola, Febiola Fendyanto Fendyanto Fendyanto, Fendyanto Fraderic, Fedelis Franclin, Marcell Gabriella, Theresia Geasela, Yemima Monica Geasela, Yemima Monica Hansen Handoko, Hie Hansen, Yoel Hartono, Sherren Hazimah, Malahayati Hendri, Glenda Jocelyn Hon, Peter Raymond Indradjaja, Reynaldi Jen, Phangestin Johanes Shane Kaunang, Fergie Joanda Kaunang, Fergie Joanda Kinasih, Putri Rindu Kosasi Kosasi Lirungan, Maria Carol Stephanie Lukman, Nicholas Merryana Lestari Michael Wijaya Monica Geasela, Yemima Mulyanto, Andrew Kristanto Petrisa, Sisilia Ranny, Ranny Ranny, Ranny Rarasati, Dionisa Bhisetya Reji Darmawan Rendy Sarudin Rendy Sarudin Salim, Jonathan Sophia Reni Susilo Susanto, Cornelius Thenata, Angelina P. Thenata, Angelina Pramana Vinsensius Sugito Lein Wendy, Albert Widjaja, Florence Aurelia Wijayanti, Theresia Puspa