Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Aptisi Transactions on Technopreneurship (ATT) International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application
Li Wei Ming
Ijiis Incorporation
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Library & Information Science Social Sciences Other

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

Found 3 Documents
Search

 1 
AI as a Driver of Efficiency in Waste Management and Resource Recovery Li Wei Ming; James Anderson; Farhan Hidayat; Firdaus Dwi Yulian; Nanda Septiani
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.547

Abstract

Effective waste management and resource recovery are essential for maintaining environmental sustainability. With the increasing volume of waste generated from industrial and domestic activities, there is a critical need for strategies that reduce environmental impact and enhance resource utilization efficiency. This study explores the application of artificial intelligence (AI) technologies, specifically Machine Learning (ML) and Artificial Neural Networks (ANN), in optimizing waste management processes. The research demonstrates that AI can significantly improve waste classification accuracy, predict waste volumes, and identify resource recovery opportunities. Implementing AI solutions resulted in a 15% increase in resource recovery efficiency and a 20% reduction in operational costs. These findings provide valuable insights for stakeholders and policymakers in integrating AI technologies to achieve more sustainable waste management practices.
Optimizing Digital Marketing Strategies through Big Data and Machine Learning: Insights and Applications Dwi Andayani; Muchlishina Madani; Harry Agustian; Nanda Septiani; LI Wei Ming
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.29

Abstract

In the dynamic realm of digital marketing, the convergence of Big Data and machine learning has ushered in transformative changes, reshaping strategies through advanced data analytics and predictive modeling. This paper examines the pivotal role of these technologies in enhancing marketing practices, focusing on their impact on consumer targeting, engagement, and overall campaign effectiveness. By harnessing vast datasets and applying sophisticated machine learning algorithms, marketers can now predict consumer behavior with unprecedented accuracy, personalize marketing messages, and optimize operational strategies to maximize engagement and return on investment. Despite the profound advantages, the integration of these technologies raises substantial challenges, including data privacy concerns and the need for specialized skills. Through a mixed-methods approach combining quantitative data analysis and qualitative interviews, this study not only demonstrates the improved predictive accuracy and segmentation capabilities afforded by these technologies but also discusses the barriers to their full potential realization. The findings highlight a clear trajectory towards more data-driven, responsive marketing paradigms, suggesting a future where digital marketing strategies are increasingly informed by insights derived from Big Data and machine learning. This paper aims to provide a comprehensive overview of the current landscape and future potential of these transformative technologies in digital marketing.
Geometry Learning Pathways for Creative Thinking via Edupreneurial Approaches Hendri Handoko; Stevanus Budi Waluya; Wardono Wardono; Sugiman Sugiman; Li Wei Ming
Aptisi Transactions On Technopreneurship (ATT) Vol 8 No 2 (2026): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v8i2.723

Abstract

This study highlights the importance of creative thinking as a 21st-century competency that is closely connected to technopreneurship and edupreneurship, emphasizing how students’ cognitive styles Field Independent (FI) and Field Dependent (FD) shape their creative performance in geometry learning. The objective of this research is to design and describe a learning trajectory that fosters creative thinking by integrating cognitive diversity into geometry education. Using a descriptive qualitative approach, six purposively selected students representing FI and FD groups with high, medium, and low ability levels were examined through the Group Embedded Figures Test (GEFT), creative thinking tests, and semi-structured interviews. Data analysis followed the Miles and Huberman model, comprising data collection, reduction, display, and conclusion. The findings show that FI students exhibit stronger creative thinking skills across fluency, flexibility, originality, elaboration, and evaluation, while FD students particularly those with medium and low abilities, experience challenges in conceptual understanding and visual-spatial reasoning, indicating that creative thinking in geometry is strongly tied to spatial imagination. The study concludes that the proposed learning trajectory enhances creative education by linking cognitive styles with innovative learning design, offering practical contributions for curriculum development, teacher training, and educational product creation. It further supports the cultivation of innovation-oriented mindsets essential for building edupreneurial ecosystems and aligns with Sustainable Development Goal 4 (Quality Education).
Co-Authors Dwi Andayani Farhan Hidayat Firdaus Dwi Yulian Harry Agustian Hendri Handoko James Anderson Muchlishina Madani Nanda Septiani Stevanus Budi Waluya Sugiman Sugiman Wardono Wardono