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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) JUTEI (Jurnal Terapan Teknologi Informasi) TESLA: Jurnal Teknik Elektro Journal Zetroem
Hugeng
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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Journal : JUTEI (Jurnal Terapan Teknologi Informasi)

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Analisis Penghematan Daya Listrik dengan Implementasi FX-80 Supervisor Controller pada Sistem Water Chiller Yoshua, Axel; Wahab, Wahidin; Hugeng
Jurnal Terapan Teknologi Informasi Vol 8 No 2 (2024): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2024.82.358

Abstract

This research aims to analyze electrical power consumption and cooling system efficiency between the FX-80 Supervisor Controller and a system without a controller in the field. Measurements are carried out for one year by taking data on electrical power consumption and Coefficient of Performance (COP) every month. The research results show that the system with the FX-80 Supervisor Controller consumes lower electrical power and has a higher COP than the system without the controller.  The higher efficiency of systems with controllers represents significant energy savings potential for companies. This research recommends the use of controllers in cooling systems to increase energy efficiency.
Analisis Penggunaan Sumber Daya Pada Jetson Nano Untuk Sistem Pengenalan Wajah Wijaya, Dion Dwi; Hugeng; Utama, Hadian Satria
Jurnal Terapan Teknologi Informasi Vol 8 No 2 (2024): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2024.82.374

Abstract

Jetson Nano, a Single-Board Computer or SBC developed by NVIDIA, is used to implement a face recognition system that requires large computing resources. This research aims to analyze the resource usage on the Jetson Nano during the running of the face recognition system, including CPU usage, GPU, memory, and power consumption. Monitoring data was obtained using Jtop software to record real-time resource usage and a wattmeter for electrical power consumption. The results showed that the Jetson Nano CPU performed consistently with an average utilization of 77.14%, reflecting an even distribution of workload. The GPU showed an average utilization of 44.05% with higher fluctuations, indicating variations in graphics workload intensity. Memory was used close to the maximum capacity of 4 GB, with an average utilization of 3.75 GB, indicating efficient memory management. Average power consumption was recorded at 8.56 Wh, confirming the energy efficiency of this device. This study concludes that the Jetson Nano is capable of running the facial recognition system stably and efficiently, although there is room for further optimization on GPU load distribution and memory management. With its high power efficiency, the Jetson Nano is an ideal solution for artificial intelligence-based applications with low power requirements.
Co-Authors Adrianto, Christopher Calvin Andre Blenski Calvin Charlie William Danielo Endah Setyaningsih Hadian Satria Utama Johan Tjung Jolie Felicia Joni Fat Joni Fat lamto widodo Meirista Wulandari, Meirista Rafael Saktiaji Prakoso Suraidi TRI SUTRISNO Wahidin Wahab Wijaya, Dion Dwi William, Charlie Yoshua, Axel