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All Journal Building of Informatics, Technology and Science
Andiety, Rich
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Computer Science & IT

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Journal : Building of Informatics, Technology and Science

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Content-Based Music Recommender System Using Deep Neural Network Baizal, Z. K. A.; Andiety, Rich
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5762

Abstract

Music is one of the most popular forms of entertainment. Along with the development of information technology, music streaming platforms such as Spotify, Apple Music, and Deezer are increasingly popular among users. However, with thousands of songs available on these music streaming platforms, users often have difficulty finding songs that suit their tastes. Therefore, we design a music recommender system that can assist users in finding songs that are more in line with user preferences. In this research, we propose the development of a content-based music recommender system using a combination of Content-Based Filtering and Deep Neural Network (DNN) methods. The DNN used is Convolutional Neural Network (CNN) which serves to increase the percentage of accuracy to provide results that match user needs. This research aims to develop a music recommender system that can provide personalized recommendations to users according to the preferences of users. This research provides an accuracy result of 73.5%. From these results, it has been proven that the resulting music recommendations can be an alternative to the existing Collaborative Filtering-based recommender system.
Co-Authors ZK Abdurahman Baizal