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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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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.
Benchmark Analysis of Sampling Methods for RRT Path Planning Pratama, Gilang Nugraha Putu; Dhewa, Oktaf Agni; Priambodo, Ardy Seto; Baktiar, Faris Yusuf; Prasetyo, Rizky Hidayat; Jati, Mentari Putri; Hidayatulloh, Indra
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.132

Abstract

Path planning is a crucial aspect of mobile robot navigation, ensuring that robots can safely travel from their initial position to the goal. In real-world applications, path planning is essential for autonomous vehicles, drones, warehouse robots, and rescue robots to navigate complex environments efficiently and safely. One effective method for path planning is the Rapidly-exploring Random Tree (RRT) algorithm, which is particularly practical in maze-like environments. The performance of RRT depends on the sampling methods used to explore the maze. Sampling methods are important because they determine how the algorithm explores the search space, affecting the efficiency and success of finding an optimal path. Poor sampling can lead to suboptimal or infeasible paths. In this study, we investigate different sampling strategies for RRT, specifically focusing on uniform sampling, Gaussian sampling, and the Motion Planning Network (MPNet) sampling. MPNet leverages a neural network trained on past environments, allowing it to predict promising regions of the search space quickly, unlike traditional methods like RRT that rely on random exploration without prior knowledge. This makes MPNet much faster and more efficient, especially in complex or high-dimensional spaces. Through a benchmarking analysis, we compare these methods in terms of their effectiveness in generating feasible paths. The results indicate that while all three methods are effective, MPNet sampling outperforms uniform and Gaussian sampling, particularly in terms of path length. The mean path length generated, based on a sample size of 30, is 13.115 meters for MPNet, which is shorter compared to uniform and Gaussian sampling, which are 18.27 meters and 18.088 meters, respectively. These findings highlight the potential to enhance path planning algorithms using learning-based sampling methods.
Optimalisasi Potensi Dusun Terbah Serut Kulon Progo melalui Media Digital Berbasis Website Karyono, Tri Hadi; Dhewa, Oktaf Agni; Baktiar, Faris Yusuf; Priambodo, Ardy Seto; Hakim, Septian Rahman
Jurnal Abdi Masyarakat Indonesia Vol 4 No 6 (2024): JAMSI - November 2024
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.1434

Abstract

Keterbatasan akses informasi di Dusun Terbah dan Serut menghambat promosi potensi lokal. Teknologi digital, seperti website, menawarkan solusi untuk memperluas akses informasi. Melalui website, masyarakat dapat dengan mudah mengakses dan menyebarkan informasi. Kegiatan pengabdian ini bertujuan membangun sistem informasi berbasis website dan melakukan transfer pengetahuan untuk pengelolaannya. Website ini diharapkan mampu menyajikan informasi kegiatan dusun serta mempromosikan potensi lokal secara cepat dan akurat. Teknologi yang digunakan adalah CMS Wordpress dengan MySQL sebagai basis data, dan website di-hosting agar dapat diakses kapan saja. Pengabdian ini mencakup dua aktivitas utama: pengembangan aplikasi selama tiga bulan dan pelatihan melalui empat kali workshop di kantor dusun. Hasilnya, website berhasil meningkatkan visibilitas potensi lokal serta keterlibatan masyarakat. Pelatihan berjalan lancar dan mendapat respons positif dari peserta, termasuk kepala dukuh dan pengelola, yang kini lebih sadar bahwa website bukan hanya alat informasi, tetapi juga media promosi. Kegiatan ini mendorong pengelola dusun untuk lebih aktif dalam mengelola dan mempromosikan potensi lokal.
Co-Authors Ardy Seto Priambodo Gilang Nugraha Putu Pratama Indra Hidayatulloh Mentari Putri Jati Oktaf Agni Dhewa Rizky Hidayat Prasetyo Septian Rahman Hakim Tri Hadi Karyono