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Yaoxiang Mei Olivia
Faculty of Science and Technology, Suzhou University of Science and Technology
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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Artificial Intelligence in Smart Waste Management: International Applications, Challenges, and Opportunities for Indonesia Abdullah Muhammad Rotibbul Haddad; Yaoxiang Mei Olivia; Liao Hong-Yuan Mark; Huang An-Ning
Jurnal Penelitian Sains Teknologi Vol. 3, No 1, January 2027
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v3i1.18999

Abstract

Indonesia faces significant waste management challenges due to the high volume of unmanaged waste, inefficient sorting, and continued reliance on conventional disposal practices. This study aims to examine the role and implementation of Artificial Intelligence (AI) in waste sorting and management and its potential application in Indonesia. A Narrative Literature Review (NLR) was conducted by analyzing relevant studies obtained through Google Scholar using keywords related to AI, waste management, computer vision, and waste classification. The findings indicate that AI has been applied to automated waste identification, classification, real-time detection, monitoring, and robotic sorting. Applications of computer vision, deep learning, object detection, and robotic systems demonstrate potential to improve sorting accuracy, processing speed, operational efficiency, and recycling. Comparative evidence from Bangladesh, Vietnam, India, Russia, and the United Kingdom shows diverse approaches to AI-based waste management. For Indonesia, AI adoption should be adapted to local waste characteristics, infrastructure, datasets, and human resource capacity. A gradual integration of AI with digital infrastructure and automated systems can support more efficient and sustainable waste management.
Co-Authors Abdullah Muhammad Rotibbul Haddad Huang An-Ning Liao Hong-Yuan Mark