IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK FOR PRIORITIZATION OF WASTE HANDLING BASED ON DENSITY LEVEL IN IMAGE PROCESSING-BASED ONLINE WASTE REPORTING SYSTEM

Abstract

Rapid population growth and urbanization in Indonesia has led to a significant increase in waste volume. in waste volume. Data from the Central Statistics Agency (BPS) recorded waste volume to reach 64 million tons per year by 2023. High urbanization high urbanization is driving waste production in urban areas, while the transformation of consumption patterns, especially towards single-use products and plastics, are also contribute to this increase. Effective waste management must pay attention to the quality and quantity of its management, and be supported by relevant technological innovations. relevant technological innovations. However, limited resources, both budget and manpower, are the main obstacles. Despite the launch of the Online Waste Reporting System (SPSO) has been launched, challenges such as the lack of an automation mechanism and integration with local automation and integration with local waste management systems are still slowing down the response to the waste problem. slowing down the response to the waste problem. Technological innovations such as Convolutional Neural Network (CNN) and image processing promise a solution to improve the efficiency and to improve efficiency and responsiveness in waste management, with the potential to speed up identification and response to waste reports more accurately and efficiently. more accurately and efficiently. Good coordination between government and related agencies as well as improved distribution of resources are needed to address these challenges and improve the effectiveness of waste management in Indonesia.