Identification of Organic and Non-Organic Waste with Computer Image Recognition using Convolutionalneural Network with Efficient-Net-B0 Architecture - Sutomo, Heny Indriani (LPPM Universitas Dian Nuswantoro, 2023)
This study aims to develop a method for identifying organic and non-organic waste using a computer image recognition technique based on Convolutional Neural Network (CNN) with Efficient-Net-B0 architecture. Efficient and accurate waste identification is important in sustainable waste management. The primary goal of this research is to distinguish between organic and non-organic waste in images. Manually labeling waste images as organic or non-organic can be a time-consuming and error-prone task. Configuring and fine-tuning the EfficientNet-B0 architecture and CNN parameters for optimal performance can be a complex and iterative process. Hyperparameter tuning may be needed. Ensuring accurate labels is essential for training a reliable model. The choice of using the Convolutional Neural Network (CNN) with the EfficientNet-B0 architecture is a crucial part of the solution. EfficientNet-B0 is known for its balance between accuracy and computational efficiency. The use of CNNs and EfficientNet-B0 for this task indicates the system's ability to discern visual differences between the two waste types. The method proposed in this study utilizes CNN's ability to study important features of waste images to recognize various types of waste. This research includes the waste data collection stage which includes organic and non-organic waste in the form of 2D images. To evaluate the performance of the proposed method, a test was carried out using a waste dataset taken from a predetermined environment. The test results show that the proposed method is able to identify organic and non-organic waste with a high degree of accuracy. In test scenarios, this method achieves an accuracy of 98%, which demonstrates its ability to effectively identify the type of waste. Through the use of CNN-based computer image recognition techniques with the Efficient-Net-B0 architecture, this research succeeded in solving the problem of identifying organic and non-organic waste automatically and accurately. The proposed method has the potential to be applied in more efficient waste management systems, helps minimize human identification errors, and makes a positive contribution to environmental protection efforts. This research is expected to be the basis for further development in the introduction and management of waste in a sustainable manner.