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ARTIFICIAL RESEARCH: Artificial Intelligence and Robotic Journal
Published by Siber Nusantara Research & Yayasan Sinergi Inovasi Bersama
ISSN : -     EISSN : 31094538     DOI : https://doi.org/10.38035/artificial
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
KRYPTON RESEARCH: Artificial Intelligence and Robotic Journal adalah jurnal ilmiah yang diterbitkan oleh Siber Nusantara Publisher dan berada di bawah naungan Yayasan Sinergi Inovasi Bersama (SIBER). Jurnal ini terbit dua kali dalam setahun. Ruang lingkup dan fokus jurnal ini mencakup penelitian-penelitian ilmiah dengan pendekatan multidisipliner yang berfokus pada bidang Kecerdasan Buatan (Artificial Intelligence) dan Robotika (Robotics), serta aplikasinya dalam berbagai sektor. Topik-topik yang termasuk dalam cakupan jurnal ini antara lain: Machine Learning & Deep Learning Natural Language Processing (NLP) Computer Vision dan Image Processing Neural Networks & Fuzzy Logic Internet of Things (IoT) berbasis AI Robotic Process Automation (RPA) Autonomous Systems & Intelligent Robotics Humanoid Robots dan Industrial Robots Big Data Analytics dalam sistem AI Embedded Systems & Sensor Networks Smart Systems dan Smart Environments Human-Robot Interaction (HRI) Sistem Pakar dan Pengambilan Keputusan Otomatis Integrasi AI dalam bidang Kesehatan, Pertanian, Pendidikan, Transportasi, dan Industri Etika dan Regulasi dalam Pengembangan AI dan Robotika Pengembangan perangkat lunak dan perangkat keras berbasis AI Teknologi simulasi dan sistem cerdas lainnya Jurnal ini terbuka bagi peneliti, akademisi, praktisi, serta mahasiswa dari berbagai disiplin ilmu yang ingin mempublikasikan karya ilmiah terkait pengembangan dan penerapan kecerdasan buatan dan robotika dalam berbagai bidang kehidupan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 6 Documents
 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.
The Role of Data Pre-Processing Techniques and Classification Algorithms on the Accuracy of Sentiment Analysis in Social Media: A Literature Review Salsabila Dwi Fitri; Yorasakhi Ananta
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.v1i1.4

Abstract

The development of digital technology and the explosion of data on social media have increased the need for accurate sentiment analysis to understand public opinion. This article aims to systematically review the role of data pre-processing techniques and classification algorithms in improving the accuracy of sentiment analysis in social media. Through the Systematic Literature Review (SLR) approach, more than 30 scientific articles from trusted sources were reviewed between 2018 and 2024. The results of the study show that effective pre-processing such as tokenization, stemming, and stop word removal significantly improve the quality of input data, while algorithms such as SVM, Random Forest, and deep learning provide the best performance in sentiment classification. This article is expected to be a conceptual reference for further research and the development of a more precise sentiment analysis system.
Contribution of Big Data and Cloud Computing Integration to Large-Scale Data Analytics Process Efficiency: A Literature Review Yorasakhi Ananta; Salsabila Dwi Fitri
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.v1i1.5

Abstract

This article explores the contribution of Big Data and Cloud Computing integration to the efficiency of large-scale data analytics processes. Big Data technology provides the ability to manage large volumes, velocity, and variety of data, while Cloud Computing offers an elastic and scalable platform for data storage and processing. This study shows that the synergy between these two technologies improves the speed, accuracy, and efficiency of data processing, enabling organizations to make data-driven decisions faster and more precisely. The results of the reviewed literature show that the use of Cloud Computing reduces infrastructure costs and accelerates big data processing, while Big Data provides deeper insights into hidden trends and patterns. Overall, this article confirms that the integration of Big Data and Cloud Computing plays a significant role in improving the efficiency of data analytics, as well as providing a competitive advantage for organizations that can properly utilize both technologies.
Enhancing Human-Robot Collaboration through Motion Planning and Adaptive Control Systems Wahyu Kurniadi
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.v1i1.10

Abstract

The Human-robot collaboration (HRC) is increasingly becoming a major concern in the development of modern industrial technology. One of the biggest challenges in HRC is to create a system that enables safe, efficient, and adaptive interaction between humans and robots in a dynamic work environment. This article examines the role of motion planning and adaptive control systems in enhancing the effectiveness of such collaboration. By implementing motion planning algorithms that are able to respond in real-time to changes in the environment and human behavior, and adaptive control systems that adjust the robot's response to external inputs, human-robot collaboration can achieve a higher level of synergy. This study presents a modeling and simulation-based approach and case studies of collaboration in the manufacturing and service sectors. The results of the study show that the integration of motion planning and adaptive control not only improves safety and efficiency, but also strengthens the robot's ability to work side by side with humans in a natural way.
Peran Artificial Intelligence (AI) dan Teknologi Robotik dalam Transformasi Pendidikan Reni Silviah
ARTIFICIAL RESEARCH: Artificial Intelligence and Robotic Journal Vol. 1 No. 2 (2026): Artificial Intelligence and Robotic Journal (January - June 2026)
Publisher : Siber Nusantara Research & Yayasan Sinergi Inovasi Bersama (SIBER)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/artificial.v1i2.65

Abstract

Peran artificial intelligence (AI) dan teknologi robotik dalam transformasi pendidikan adalah artikel ilmiah dengan tujuan untuk menganalisa apakah artificial intelligence dan teknologi robotic berpengaruh terhadap transformasi pendidikan. Metode penelitian ini menggunakan metode literature review atau studi kepustakaan. Metode ini bertujuan untuk mengkaji, menganalisis, dan mensintesis berbagai hasil penelitian terdahulu. Hasil artikel ini adalah: 1) Artificial intelligence (AI) berperan terhadap transformasi Pendidikan, 2) Teknologi robotik berperan terhadap transformasi pendidikan. Selain dari 2 variabel exogen ini yang mempengaruhi variabel endogen prestasi siswa masih banyak faktor lain di antaranya era digital, teknologi, literasi pendidikan.

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