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All Journal Jurnal Abdi Masyarakat Indonesia Journal of Robotics, Automation, and Electronics Engineering Control Systems and Optimization Letters
Faris Yusuf Baktiar
Universitas Negeri Yogyakarta
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health

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Journal : journal of robotics automation and electronics engineering

 1 
DVFS and Timing Optimization on GPU for Data Center Computation Faris Yusuf Baktiar
Journal of Robotics, Automation, and Electronics Engineering Vol. 2 No. 1 (2024): March 2024
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jraee.v2i1.556

Abstract

Data center computing requires efficient GPU support, both in terms of functionality and power consumption. GPU performance efficiency can be reduced due to high power usage and reduced GPU work stability. So it requires an analysis of computational performance and power efficiency to improve performance and reduce power usage. Core voltage, core frequency, and memory timings are parameters that affect the efficiency of computing performance, power efficiency, and stability. Increasing computational efficiency and GPU power with the effect of modifying parameters can be done through the Basic Input-Output System (BIOS). This study analyzes the efficiency of computational performance by optimizing memory timings and analyzing power efficiency and stability by modifying the DVFS algorithm. Tests are carried out using computational benchmarks commonly used in data centers including the tessellation algorithm, rendering, image processing, pi calculation, image stitching, deep learning, molecular simulation, and N-body. The efficiency of computing performance and GPU power efficiency can be increased by optimizing memory timings and changing the voltage and frequency values on DVFS. Increased performance efficiency ranged from 33.3% to 66.7% and power efficiency increased from 19.9% to 32.6%. Modification of the DVFS voltage state can increase voltage stability and GPU core frequency stability.
Prototype of a Coffee Bean Weight Measuring Device Using a Webcam with the Convolutional Neural Network (CNN) Method at Roetin Coffee Shop Nurul Budi; Faris Yusuf Baktiar
Journal of Robotics, Automation, and Electronics Engineering Vol. 4 No. 1 (2026): March 2026
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jraee.v4i1.2375

Abstract

The advancement of Artificial Intelligence (AI) and Computer Vision has enabled new opportunities for automation within the coffee industry, particularly in weight measurement of coffee beans, which is still performed manually and becomes inefficient at large scale. This study proposes an automatic weight estimation system using images captured by a webcam and processed through a Convolutional Neural Network (CNN) employing MobileNet as a lightweight regression model. The developed system analyzes visual features to estimate weight autonomously, offering an efficient, contactless alternative to conventional weighing tools and supporting stock monitoring for coffee industries and small enterprises.
Co-Authors Ardy Seto Priambodo Gilang Nugraha Putu Pratama Indra Hidayatulloh Mentari Putri Jati Nurul Budi Oktaf Agni Dhewa Rizky Hidayat Prasetyo Septian Rahman Hakim Tri Hadi Karyono