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All Journal Sebatik J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Informasi Interaktif Jurnal Tekinkom (Teknik Informasi dan Komputer) Community Development Journal: Jurnal Pengabdian Masyarakat Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Journal of Information Technology (JIfoTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Edukasia: Jurnal Pendidikan dan Pembelajaran Epmas: Edukasi dan Pengabdian Masyarakat
Listra Firgia
Institut Shanti Bhuana
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Other

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Journal : J-SAKTI (Jurnal Sains Komputer dan Informatika)

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Eksplorasi Deep Learning Menghasilkan Karya Musik Menggunakan Metode Generative Adversarial Networks (GANS) (Kasus Musik Genre Pop) Noviyanti. P; Y Yuliana; Listra Firgia; Veneranda Rini Hapsari
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.705

Abstract

Music artistry is an enduring form of artistic expression that continues to evolve across various genres. Among these genres, pop music stands out as particularly popular. Creating musical compositions is a challenging endeavor, requiring a profound understanding of musical notation, a skill possessed by select individuals, such as musicians. Even for musicians, a wealth of references is necessary to produce fresh compositions that can be appreciated by a wide audience. This study aims to explore the creation of new pop genre music using Generative Adversarial Networks (GANs). GANs, a widely adopted method, demonstrate the capability to generate novel works by leveraging two distinct components: the Generator and the Discriminator. These models engage in a competitive interplay, with the Generator striving to produce synthetic datasets that closely resemble authentic ones, while the Discriminator endeavors to discern between datasets generated by the Generator and genuine ones. Based on the conducted research, it is evident that GANs have the capacity to generate a diverse range of new music based on acoustic piano instrument notations, employing a dataset of 50 music files in .mid format.
Co-Authors Ag. Rudatyo Himamunanto Agustinus Rudatyo Himamunanto Azril Christian Nurcahyo Candra Gudiato Fra Siskus Dian Arianto Hammad, Rifqi M Mira Manggu, Blasius Margaretha Lidya Sumarni Maya Sari Mira Mira Noviyanti P Noviyanti. P Noviyanti. P Nurcahyo, Azriel Christian Sabinus Beni Shanti Thomas Siprianus Jewarut Soedjatmiko Veneranda Rini Hapsari Vuspitasari, Benedhikta Kikky Winda Lidya Lumbantobing Y Yuliana Yulianto Mustaqim