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This Author published in this journals
All Journal Journal of Applied Data Sciences Startupreneur Business Digital (SABDA Journal) International Transactions on Artificial Intelligence (ITALIC)
Chen, Shih-Chih
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science

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The Empirical Study of Usability and Credibility on Intention Usage of Government-to-Citizen Services Cheng, Tsang-Hsiang; Chen, Shih-Chih; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 2, No 2: MAY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i2.30

Abstract

E-government allows governments to service citizens in a more timely, effective, and cost-efficient method. The most popular benefits of Government-to-Citizen (G2C)are the simple posting of forms and registrations, serve citizens, improvement of education information and e-voting. This paper analyzes the influence of website usability and the credibility on both citizen satisfaction and citizen intention to use an e-government website, as well as the impact of citizen satisfaction on citizen intentions. To prove the validity of our proposed research model, empirical analysis was performed with 366 valid questionnaires using Partial Least Square. The results of the research show that credibility of website e-government usage had significant effects on citizen satisfaction which in turn affects citizen intention to use, and citizen satisfaction also significantly affected citizen intention to use. However, the usability of e-government websites slightly influences citizen satisfaction and citizen intention to use.
The Role of Machine Learning in Improving Robotic Perception and Decision Making Chen, Shih-Chih; Pamungkas, Ria Sari; Schmidt, Daniel
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.661

Abstract

Machine learning, specifically through Convolutional Neural Networks (CNNs) and Reinforcement Learning (RL), significantly enhances robotic perception and decision-making capabilities. This research explores the integration of CNNs to improve object recognition accuracy and employs sensor fusion for interpreting complex environments by synthesizing multiple sensory inputs. Furthermore, RL is utilized to refine robots real-time decision-making processes, which reduces task completion times and increases decision accuracy. Despite the potential, these advanced methods require extensive datasets and considerable computational resources for effective real-time applications. The study aims to optimize these machine learning models for better efficiency and address the ethical considerations involved in autonomous systems. Results indicate that machine learning can substantially advance robotic functionality across various sectors, including autonomous vehicles and industrial automation, supporting sustainable industrial growth. This aligns with the United Nations Sustainable Development Goals, particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 8 (Decent Work and Economic Growth), by promoting technological innovation and enhancing industrial safety. The conclusion suggests that future research should focus on improving the scalability and ethical application of these technologies in robotics, ensuring broad, sustainable impact.
Advancing Production Management through Industry 4.0 Technologies Chen, Shih-Chih; Yati, Istiqomah; Beldiq, Eiser Aaron
Startupreneur Business Digital (SABDA Journal) Vol. 3 No. 2 (2024): Startupreneur Business Digital (SABDA)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v3i2.637

Abstract

The development of Industry 4.0 technologies has brought significant changes to various sectors, including production management. In increasingly complex production environments, the adoption of Industry 4.0 technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics has become crucial for enhancing the efficiency and effectiveness of production pro- cesses. This study aims to explore how Industry 4.0 technologies can be applied in production management to optimize operational performance and reduce re- source wastage. The research employs a quantitative approach with data analysis obtained from case studies of manufacturing companies that have implemented Industry 4.0 technologies. Data collection was conducted through surveys and interviews with production managers, along with the analysis of operational per- formance reports. The results indicate that the implementation of Industry 4.0 technologies significantly improves production efficiency, accelerates response times to market demand changes, and reduces operational costs. These findings suggest that integrating Industry 4.0 technologies into production management can be an effective strategy for addressing the challenges of global competi- tion and changing market dynamics. This research contributes to the production management literature by offering insights into the positive impact of adopting Industry 4.0 technologies in operational contexts. The results are expected to serve as a reference for practitioners in developing more adaptive and innovative production strategies in the digital era.
Co-Authors Beldiq, Eiser Aaron Cheng, Tsang-Hsiang Pamungkas, Ria Sari Schmidt, Daniel Taqwa Hariguna Yati, Istiqomah