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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Firgia, Listra
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Eksplorasi Deep Learning Menghasilkan Karya Musik Menggunakan Metode Generative Adversarial Networks (GANS) (Kasus Musik Genre Pop) P, Noviyanti.; Yuliana, Y; Firgia, Listra; Hapsari, Veneranda Rini
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.
Eksplorasi Deep Learning Menghasilkan Karya Musik Menggunakan Metode Generative Adversarial Networks (GANS) (Kasus Musik Genre Pop) P, Noviyanti.; Yuliana, Y; Firgia, Listra; Hapsari, Veneranda Rini
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.
Deteksi Jenis Penyakit Dan Hama Pada Tanaman Jagung Menggunakan Arsitektur Spatial Pyramid Pooling Pada YOLOv5s Mira, M; Firgia, Listra; Thomas, Shanti
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i2.630

Abstract

Corn is one of the important crops in the agricultural sector in the global and national economy because it is a food resource as food, animal feed and other raw materials for the community. Based on satudata.pertanian.go.id, the projected corn production in 2020-2024 will still increase between 0.94% and 0.97% per year. In this study, detection of diseases and pests in maize was carried out using YOLO technology with spatial pyramid pooling (SPP) architecture as a form of intelligent innovation in maize farming. The research data consisted of 309 image data with class values as labels representing types of disease and types of pests in corn plants consisting of Locusta (Locust), Sitophilus (Powder Flower), Spodoptera (Arrayworm), Mysus Persicase (Aphids), and Bulai . The indicators for testing and evaluating the model use precision, recall, f1 score, mAP0.5 and Map0.5:0.95 as evaluation metrics. Based on the results of training and evaluation of the model, it is known that the precision value with batch size 32 epoch 64 produces a precision value of 0.65, recall, 0.76, f1 score 0.65 Map0.5 0.704 and Map-.5:0.95 0.298. Whereas with a batch size of 64 epoch 100 the precision value is 0.73, the recall is 0.77 f1 score is 0.73 Map0.5 0.795 and Map0.5:0.95 0.346. Model predictions using YOLO technology with spatial pyramid pooling architecture in detecting types of diseases and pests in corn plants contribute to smart agriculture. With accurate information about the types of diseases and pests that attack corn plants, farmers can respond quickly and take appropriate actions, such as using specific pesticides or suitable organic control methods.
Co-Authors Hapsari, Veneranda Rini Mira, M P, Noviyanti. Thomas, Shanti Yuliana, Y