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All Journal Venus: Jurnal Publikasi Rumpun Ilmu Teknik Prosiding Seminar Nasional Ilmu Pendidikan
Zada Aulia Munawarah
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Arts Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Public Health Social Sciences

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Simulasi Protokol TCP-UDP Pada Jaringan Topologi Bus Menggunakan Simulasi Cisco Packet Tracer Qonita Auliani; Zada Aulia Munawarah; Muhammad Rizki; Riyan Aditya; Didik Aribowo
Venus: Jurnal Publikasi Rumpun Ilmu Teknik  Vol. 2 No. 3 (2024): Juni : Jurnal Publikasi Rumpun Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/venus.v2i3.330

Abstract

In this digital era, network speed is very important, because nowadays many people use the internet as a companion in various events. Data communication is one of the means to allow several devices to communicate with each other. To simplify and speed up network usage, the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) protocols are used as the transport layer during data transfer. In running this simulation using the Cisco Packet Tracer simulation application using bus topology as the topology type.
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 Didik Aribowo Muhammad Rizki Novaldi Ramdani Reza Qonita Auliani Rachmatul Hidayathika Riyan Aditya Umar Hamzah