Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) Wellness And Healthy Magazine J-SAKTI (Jurnal Sains Komputer dan Informatika)
Yuliana, Y
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Corona virus diseases (Covid-19): Sebuah tinjauan literatur Yuliana, Y
Wellness And Healthy Magazine Vol 2, No 1 (2020): February
Publisher : Universitas Aisyah Pringsewu (UAP) Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (67.739 KB) | DOI: 10.30604/well.95212020

Abstract

Coronavirus Disease (Covid-19). In 2020, a new type of coronavirus (SARS-CoV-2) was spread, called a disease called Coronavirus disease 2019 (COVID-19). This virus was discovered in Wuhan, China for the first time and has infected 90,308 people as of March 2, 2020. The number of deaths reached 3,087 people or 6%, the number of patients recovering 45,726 people. This type of single positive RNA strain infects the human respiratory tract and is sensitive to heat and can effectively be activated by chlorine-containing disinfectants. The source of the host is thought to come from animals, especially bats, and other vectors such as bamboo rats, camels and ferrets. Common symptoms include fever, cough and difficulty breathing. Clinical syndrome is divided into uncomplicated, mild pneumonia and severe pneumonia. Specimen examination is taken from the throat swab (nasopharynx and oropharynx) and lower airway (sputum, bronchial rinse, endotracheal aspirate). Isolation was carried out on patients proven to be infected with Covid-19 to prevent wider spread. Abstrak: Penyakit Virus Corona (Covid-19) tahun 2020 merebak virus baru coronavirus jenis baru (SARS-CoV-2) yang penyakitnya disebut Coronavirus disease 2019 (COVID-19). Virus ini ditemukan di Wuhan, China pertama kali dan sudah menginfeksi 90.308 orang per tanggal 2 Maret 2020. Jumlah kematian mencapai 3.087 orang atau 6%, jumlah pasien yang sembuh 45.726 orang. Virus jenis RNA strain tunggal positif ini menginfeksi saluran pernapasan manusia dan bersifat sensitif terhadap panas dan secara efektif dapat diinaktifkan oleh desinfektan mengandung klorin. Sumber host diduga berasal dari hewan terutama kelelawar, dan vektor lain seperti tikus bambu, unta dan musang. Gejala umum berupa demam, batuk dan sulit bernapas. Sindrom klinik terbagi menjadi tanpa komplikasi, pneumonia ringan dan pneumonia berat. Pemeriksaan spesimen diambil dari swab tenggorok (nasofaring dan orofaring) dan saluran napas bawah (sputum, bilasan bronkus, aspirat endotrakeal). Isolasi dilakukan pada pasien terbukti terinfeksi Covid-19 untuk mencegah penyebaran lebih luas.
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.
Co-Authors Firgia, Listra Hapsari, Veneranda Rini P, Noviyanti.