Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal ARTIFICIAL RESEARCH: Artificial Intelligence and Robotic Journal
Ridwan Ridwan
Sekolah Tinggi Teknologi Pekerjaan Umum, Jakarta, Indonesia
Computer Science & IT Control & Systems Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Increased Operational Efficiency through Programming Optimization and Scheduling of Collaborative Robot Tasks Ridwan Ridwan
ARTIFICIAL RESEARCH: Artificial Intelligence and Robotic Journal Vol. 1 No. 1 (2025): Artificial Intelligence and Robotic Journal (July - Desember 2025)
Publisher : Siber Nusantara Research & Yayasan Sinergi Inovasi Bersama (SIBER)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/artificial.v1i1.2

Abstract

This study aims to optimize the programming and task scheduling of collaborative robots (cobots) in industrial settings to improve operational efficiency, productivity, and cost-effectiveness. The research employs a descriptive-experimental design to evaluate the impact of optimization techniques on cobot performance in a manufacturing environment. Key performance indicators (KPIs) such as productivity, operational costs, task completion time, and resource utilization efficiency were analyzed before and after the implementation of optimization techniques, including linear programming, genetic algorithms, and heuristic scheduling. The results revealed a 23% improvement in productivity, a 20% reduction in operational costs, a 25% reduction in task completion time, and a 20% improvement in resource utilization efficiency. These improvements highlight the potential of optimizing cobot programming and task scheduling to significantly enhance industrial operations. The study also discusses the challenges of integrating optimization techniques into existing production systems and the need for continuous monitoring to maintain efficiency. This research contributes valuable insights into the role of cobots in modern manufacturing and provides practical recommendations for industries seeking to enhance operational efficiency through automation. Future studies are suggested to explore more advanced optimization techniques, including machine learning-based approaches, to further improve the performance and adaptability of collaborative robots in various industrial environments.
Building Artificial Intelligence Algorithms to Help Human Work Effectively Sugiyatno Sugiyatno; Ridwan Ridwan
ARTIFICIAL RESEARCH: Artificial Intelligence and Robotic Journal Vol. 1 No. 1 (2025): Artificial Intelligence and Robotic Journal (July - Desember 2025)
Publisher : Siber Nusantara Research & Yayasan Sinergi Inovasi Bersama (SIBER)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/artificial.v1i1.3

Abstract

This study aims to develop Artificial Intelligence (AI) algorithms that can effectively assist human work across various industrial sectors. By leveraging AI's ability to automate routine tasks, support decision-making, and enhance human-machine collaboration, this research demonstrates AI's potential to improve work efficiency and productivity. The study tests the implementation of AI algorithms in three key industries: manufacturing, healthcare, and customer service, focusing on optimizing task scheduling, enhancing decision-making quality, and improving human-machine collaboration. The results show that the implementation of AI can reduce task completion time, improve diagnostic accuracy in healthcare, speed up customer response times, and increase worker satisfaction. In the manufacturing sector, task completion time decreased by up to 41%, while in healthcare, diagnostic accuracy improved by 17%. Furthermore, worker satisfaction significantly increased after AI implementation, with 56% of workers reporting being "Highly Satisfied" with AI collaboration, compared to 30% before implementation. It is expected that the findings of this research will provide insights into how AI can enhance work quality and efficiency in the workplace, while supporting workers in completing more complex and creative tasks.
Co-Authors Sugiyatno Sugiyatno