Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Blockchain Frontier Technology (BFRONT) International Transactions on Artificial Intelligence (ITALIC)
Oscar Jayanagara
University of Pelita Harapan
Computer Science & IT Control & Systems Engineering Education Library & Information Science

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
An Overview of Concepts, Applications, Difficulties, Unresolved Issues in Fog Computing and Machine Learning Oscar Jayanagara; Dewi Sri Surya Wuisan
International Transactions on Artificial Intelligence Vol. 1 No. 2 (2023): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v1i2.318

Abstract

Numerous fog computing apps and services are emerging as a result of the large volumes of data produced by systems based on fog computing. Additionally, the crucial field of machine learning (ML), which has made significant advancements in several academic areas, incorporating speech recognition, robotics, and neuromorphic computing, in addition to computer graphics and natural language processing (NLP).. The aim of current research provided insight into providing a list of the fog computing-related ML operations. The security, capacity, and latency standards for networks must be met by many IoT applications. Cloud computing does not, however, satisfy these needs. Today's technology can satisfy these objectives, and edge computing is one such option. The model enables traffic analysis and sensor data analysis. The management of resources, accuracy, and security are three areas of fog computing that we highlight in this thorough assessment of the most recent advancements in ML approaches. Additionally highlighted is the function of ML in edge computing. Additional viewpoints on the ML domain are presented, including those on the different kinds of application support, techniques, and datasets. Finally, open questions and research difficulties are highlighted.
The Role of Smart Automation in Tourism Industry Management using Smart PLS Oscar Jayanagara; Sri Watini
International Transactions on Artificial Intelligence Vol. 2 No. 1 (2023): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i1.385

Abstract

The main objective of this research is to critically examine existing research and the application of smart automation (Smart PLS) in the tourism industry. Additionally, this study proposes a new evaluation framework to identify vulnerabilities in the adoption of smart automation. This is a synthesis and evaluation study, where qualitative findings about the implementation of smart automation in the tourism industry are presented. The study uses a seven-dimensional evaluation framework based on Rogers' diffusion theory (2003) to assess the current implementation of intelligent automation. The vulnerability of adopting smart automation in the tourism industry depends largely on the type of smart automation used. Search/booking engines, virtual agents, and chatbots are three types of intelligent automation that have high vulnerability levels. The limitations and implications of this study are that it bridges the foundations of innovation diffusion theory and the application of smart automation. The findings from this research can help researchers, developers, and managers evaluate the vulnerability of adopting smart automation technology in the tourism industry. This paper is also one of the few papers that assess the vulnerability of adopting smart automation in the tourism industry, and it develops a theory-based evaluation framework to systematically evaluate smart automation innovation in the tourism industry.
The Effectiveness of Using Blockchain Technology as A Machine Learning Program Asep Sutarman; Evelin Kallas; Oscar Jayanagara
Blockchain Frontier Technology Vol. 4 No. 1 (2024): Blockchain Frontier Technology
Publisher : Indonesian Association of Higher Education in Informatics and Computing (APTIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v4i1.576

Abstract

 Several successful papers and applications have demonstrated machine learning's limitless potential. However, when we immerse ourselves in powerful machine learning-based systems or applications, two critical research issues arise: how to ensure that the searched results of a machine learning system are not tampered with by anyone and how to prevent other users in the same network environment from easily obtaining our private data. This predicament is similar to that of other modern information systems that face security and privacy concerns. The introduction of blockchain technology presents us with an alternate method of addressing these two challenges. As a result, recent research has sought to construct machine learning systems using blockchain technology or to apply machine learning methods to blockchain systems. In this study, we provided a parallel framework to identify acceptable deep learning hyperparameters in a blockchain context using a metaheuristic algorithm to demonstrate what the combination of blockchain and machine learning is capable of. The suggested system also takes communication costs into consideration by restricting the amount of information transfers between miners and blockchain.
Co-Authors Asep Sutarman Evelin Kallas Sri Watini Wuisan, Dewi