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

doi:10.23917/saintek.v3i1.18999

Authors

Abdullah Muhammad Rotibbul Haddad Faculty of Social and Political Sciences, Universitas Negeri Semarang
Indonesia
Yaoxiang Mei Olivia Faculty of Science and Technology, Suzhou University of Science and Technology
China
Liao Hong-Yuan Mark Department of Artificial Intelligence of Things, Institute of Information Science Academia Sinica
Taiwan, Province of China
Huang An-Ning Department of Artificial Intelligence of Things, Institute of Information Science Academia Sinica
Taiwan, Province of China

DOI:

https://doi.org/10.23917/saintek.v3i1.18999

Keywords:

artificial intelligence, intelligent waste management, automated waste sorting, computer vision, deep learning, smart waste management

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.

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