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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) G-Tech : Jurnal Teknologi Terapan REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika)
P, Noviyanti.
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Network Server Management Based on Virtualization Technology using Proxmox at Diskominfo Bengkayang Regency Sari, Maya; Nurcahyo, Azriel Christian; P, Noviyanti.
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 5, No 3 (2024): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v5i3.219-228

Abstract

Based on research conducted by the author, two server machines at Diskominfo Bengkayang District have poor resources because they use personal computers with specifications not for servers, so they cannot serve clients properly. Besides that, there is no central data storage server for sharing data. The solution to the problem of server resources is to replace it with one unit of PC Server with high specifications and because there are two servers, the method used for this problem is Virtualization Technology. All server machines are built in virtualization technology using Proxmox. Proxmox is open-source software for running Virtual Machine. With proxmox, it can minimize the use of hardware and facilitate maintenance because it uses Web Base Management for its settings and with the construction of a data center server for value data storage and data sharing, will provide convenience in doing work. The benefits of this community service activity are to streamline time and costs in server maintenance and optimal use of resources.
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.
Predicting the Potential of Renewable Solar Energy Based on Weather Data in Indonesia Using the Random Forest Method P, Noviyanti.; Sari, Maya; Kusnanto, Kusnanto
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.7776

Abstract

Renewable energy plays a crucial role in reducing greenhouse gas (GHG) emissions. Excessive use of fossil fuels, such as coal, can produce GHG emissions that trigger extreme weather and global warming. Therefore, efforts to increase renewable energy utilization are necessary, in line with the Government Work Plan (RKP) target, which targets renewable energy contributions to reach 23% by 2025. This study aims to predict the potential for solar renewable energy in an area based on radiation, temperature, and rainfall variables. The method used is a supervised learning-based Random Forest. Weather data was obtained through the Open Meteo API, then processed by assigning weights to variables to produce output labels, which were then used in the classification process and model performance evaluation. The results showed that the Random Forest model produced an accuracy of 99.82%, with predictions of low/no potential energy being completely correct, medium energy potential experiencing only one error, and high energy potential also experiencing only one error. Based on these findings, the Random Forest method has proven effective in predicting solar power potential with high accuracy and is able to identify variables with the highest to lowest levels of importance.
Co-Authors Firgia, Listra Hapsari, Veneranda Rini Kusnanto Kusnanto Maya Sari Nurcahyo, Azriel Christian Yuliana, Y