Image Classification of Organic and Inorganic Waste Using Convolutional Neural Networks

Indonesia will become the third largest contributor of plastic waste in the world in 2024. This is due to the suboptimal management and recycling of waste. One way to reduce the accumulation of waste in the environment is through waste separation as the first step in recycling. In the field of informatics engineering, this process can be implemented using Convolutional Neural Network (CNN), a deep learning method designed to recognize and classify objects in digital images. This study aims to develop a high-accuracy CNN model for waste type classification using the TensorFlow framework. The analysis was carried out to determine the most appropriate CNN architecture in separating waste optimally. By implementing this algorithm, an automatic waste separation system can be built to support the efficiency of the recycling process. This research is expected to accelerate and simplify the waste separation process, while encouraging more effective waste management.