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Pengujian Kinerja Jaringan Topologi STAR dengan Switching: Studi Simulasi Menggunakan Cisco Packet Tracer Ardian Nurarifin; Rachmatul Hidayathika; Fiana Fiana; Rafika Desfiana; Didik Aribowo
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 2 No. 3 (2024): Mei : Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v2i3.315

Abstract

In the present day, many have used communication tools to share data as text, video, images, or sound and the like. There is a speed of data transmission depending on the delivery method, if there is a system error then the data is likely not to be sent, therefore there are various types of data managers so that there are no obstacles or errors during the data transmission process. The star topology was chosen for its advantages in network management, trouble shooting and providing reliable and easy-to-organize connectivity. The main focus is to evaluate the efficacy and effeciency of the star topology in handling different amounts of data traffic using performance characteristics including packet loss, jitter, delay, and throughput. And the use of switching in the star topology can provide optimal and stable performance under high traffic load can provide reliability and efficiency in data traffic management.
Studi Medan Elektromagnetik pada Pembangkit Listrik Tenaga Surya Yuninda Triyatne; Ardian Nurarifin; Sonata Mahardika; Umar Hamzah; Diyajeng Luluk Karlina
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 2 No. 6 (2024): November : Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v2i6.609

Abstract

This research investigates the electromagnetic field (EMF) characteristics generated by solar power plants, focusing on their potential environmental and health impacts. The study employs a dual methodology combining direct measurements using gaussmeters and computer simulations using COMSOL software to map electromagnetic fields around solar power installation components. Measurements were taken under two conditions: during solar power plant operation and non-operation, to establish comparative baseline data. The research found significant variations in electromagnetic field intensity based on operational status and distance from components, particularly near inverters and cable networks. Results indicate that EMF levels are higher during operation, with field strength decreasing exponentially with distance from the source. These findings contribute to understanding EMF distribution patterns in solar installations and provide valuable insights for implementing appropriate mitigation strategies to ensure safe operation within established exposure standards.
Analisis Pemanfaatan Teknologi Digital dalam Pemeliharaan Sistem Tenaga Listrik: Literature Review terhadap Implementasi Predictive Maintenance Fiana Fiana; Ardian Nurarifin; Muhamad Taufiq Hidayatullah Syari; Muhamad Rifki Arrosyid; Didik Aribowo
Prosiding Seminar Nasional Ilmu Pendidikan Vol. 3 No. 1 (2026): Juni: Prosiding Seminar Nasional Ilmu Pendidikan
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/prosemnasipi.v3i1.219

Abstract

The rapid advancement of digital technologies has transformed maintenance practices in electric power systems, encouraging a shift from conventional approaches toward predictive maintenance strategies. This study examines the role of digital technologies in supporting predictive maintenance through a comprehensive literature review. The research was conducted by analyzing scientific journals, conference papers, and scholarly articles discussing the application of Internet of Things (IoT), Artificial Intelligence (AI), machine learning, SCADA, and digital twin technologies in power system maintenance. The review indicates that predictive maintenance contributes to improved system reliability, reduced equipment downtime, lower maintenance expenditures, and enhanced operational efficiency through continuous monitoring and data-based decision making. Furthermore, digital technologies facilitate earlier fault identification and more effective maintenance scheduling. Despite these benefits, several challenges remain, including cybersecurity concerns, investment requirements for digital infrastructure, and the need for skilled personnel. The findings of this study provide insights that may support the development of more intelligent, efficient, and reliable power systems in the future.