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All Journal Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Prosiding Seminar Nasional Ilmu Pendidikan Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer
Rachmatul Hidayathika
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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 Public Health Social Sciences

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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.
Gelombang Elektromagnetik ELF : Inovasi Baru dalam Terapi Kesehatan Tulang Rachmatul Hidayathika; Muhammad Nabil Makarim; Muhammad Irfan Andrianto; Ade Sudrajat; Diyajeng Luluk Karlina
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 6 (2024): Desember: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i6.515

Abstract

Extremely Low Frequency (ELF) electromagnetic waves are increasingly recognized for their potential in treating bone health as a safe and effective non-invasive method. Studies have shown that ELF can stimulate osteoblast activity, increase bone density, and accelerate fracture healing. This article reviews the mechanism of ELF, its application in the treatment of osteoporosis, and its safety in clinical practice. With standardized parameters, ELF can be an innovative solution for chronic bone disorders or abnormalities, as well as providing great benefits in the medical world.
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 Ade Sudrajat Ardian Nurarifin Didik Aribowo Diyajeng Luluk Karlina Fiana Fiana Muhammad Irfan Andrianto Muhammad Nabil Makarim Novaldi Ramdani Reza Rafika Desfiana Umar Hamzah Zada Aulia Munawarah