Developing Smart Trash Management System Using IoT and Machine Learning

This study presents the development of a smart trash management system utilizing IoT technology with a machine learning approach. The aim of this research is to address the ineffectiveness of current trash management systems. The proposed method involves experimentation by collecting data from sensors connected to the IoT network and utilizing an algorithm to classify trash capacity. The system provides essential information such as trash bin capacity, humidity, temperature, and light conditions around the trash bin. Moreover, it classifies the trash capacity based on the data collected from sensors connected to the IoT network. The integration of mobile applications enables users to monitor and control the trash bin in real-time. In the testing phase, a total of 150 data samples were collected. The results of the experiments utilizing SVM, K-NN, and DT algorithms demonstrated that SVM is the most efficient algorithm for classifying trash in an intelligent trash management system using IoT. The SVM algorithm showed high accuracy, precision, and recall values of 92.3%, 90.2%, and 87.8%, respectively. This research contributes to a more effective and efficient waste management system, which helps maintain cleanliness in campus environments.