Traspoter Application Development: Website-Based Automatic Garbage Classification Using CNN Method

Bima Julian Mahardika
Budy Santoso
Aulia Anggraeni
Muhamad Ali Imron
Anatasya Wenita Putri
Endang Purnama Giri
Gema Parasti Mindara

International Journal of Multilingual Education and Applied Linguistics
Vol. 1 No. 4 (2024): November : International Journal of Multilingual Education and Applied Linguist

This research focuses on the development of automatic waste classification by applying the Convolutional Neural Network (CNN) method in a web-based application. This system is designed to help the waste management process through automatic sorting between organic and inorganic waste, so that it can support recycling efforts and reduce environmental impacts. In its application, this application utilizes the CNN algorithm to analyze images and recognize the type of waste with good accuracy. The development uses technologies such as Python and OpenCV to ensure efficient processing of image data, with the CNN model trained using a dataset of 22,564 images. Test results show excellent accuracy, reaching 99.27% for organic waste and 98.72% for inorganic waste.