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All Journal Jurnal Teknik Mesin, Industri, Elektro dan Informatika Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Prosiding Seminar Nasional Ilmu Pendidikan
Umar Hamzah
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Aerospace Engineering Arts Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Public Health Social Sciences

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Simulasi Penggunaan Cisco Packet Treaser Untuk Protokol TCP dan UDP Dalam Topologi Jaringan Ring Muhammad Abdullah Bin Matni; Raihan Ahmad Musyaffa; Umar Hamzah; Aini Nur Hayani; Didik Aribowo
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 3 No. 2 (2024): Juni : JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v3i2.3832

Abstract

This research aims to evaluate the performance of TCP and UDP protocols using Cisco Packet Tracer in a ring network topology. This simulation examines parameters such as latency, throughput, and packet loss for both protocols, and compares their effectiveness in different network scenarios. Simulation results show significant performance differences between TCP and UDP in a ring topology, with TCP showing higher reliability while UDP offers higher speed.
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.
Systematic Literature Review Penerapan Artificial Intelligence dalam Pemeliharaan Prediktif Sistem Tenaga Listrik sebagai Inovasi Pembelajaran Berbasis Teknologi Digital Rachmatul Hidayathika; Zada Aulia Munawarah; Umar Hamzah; Didik Aribowo; Novaldi Ramdani Reza
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.v3i1.220

Abstract

Artificial Intelligence (AI) has become an important technology in predictive maintenance of power systems due to its ability to improve reliability, efficiency, and asset management. Conventional maintenance approaches, such as corrective and preventive maintenance, often fail to accurately predict equipment failures, resulting in higher operational costs and unplanned outages. This study aims to analyze the development, applications, benefits, challenges, and future directions of AI in predictive maintenance of power systems. The research employed a Systematic Literature Review (SLR) based on the PRISMA framework. Literature was collected from Google Scholar using keywords related to artificial intelligence, predictive maintenance, machine learning, fault diagnosis, condition monitoring, and power systems. A total of 22 publications published between 2020 and 2025 met the inclusion criteria and were analyzed. The findings indicate that AI plays a significant role in fault detection, fault diagnosis, condition monitoring, and remaining useful life prediction of power equipment. AI has been widely applied to transformers, generators, switchgear, Photovoltaic Systems, and variable frequency drives. Furthermore, the integration of AI with IoT, Big Data Analytics, Cloud Computing, and Digital Twin technologies enhances predictive accuracy and maintenance decision-making. Overall, AI contributes significantly to improving the reliability, efficiency, and sustainability of future power systems.
Co-Authors Aini Nur Hayani Ardian Nurarifin Didik Aribowo Diyajeng Luluk Karlina Muhammad Abdullah Bin Matni Novaldi Ramdani Reza Rachmatul Hidayathika Raihan Ahmad Musyaffa Sonata Mahardika Yuninda Triyatne Zada Aulia Munawarah