Perbandingan Klasifikasi Jenis Sampah Menggunakan Convolutional Neural Network Dengan Arsitektur ResNet18 dan ResNet50 - Christin Evasari Nainggolan; Muhammad Nasir; Fatoni; Devi Udariansyah (CSRID (Computer Science Research and Its Development Journal), 2024)
Complex problems occur in dealing with waste, both in developing and developed countries, such as Indonesia. According to data from the Ministry of Environment and Forestry, in 2022 the total waste pile will reach 34,439,338.12 tons per year. In this research, machine learning will be used by comparing CNN architecture, ResNet18 with ResNet50, for the classification of waste types. This research uses 2527 images of garbage image data consisting of 6 classes, namely cardboard, glass, metal, paper, plastic and trash. Convolutional Neural Network is a component of the Deep Neural Network method that has the ability to identify objects in images with a high level of complexity. From the ResNet18 model test in this study, the accuracy was 98.69% and the test results on ResNet50 resulted in an accuracy of 99.41%. The precision and recall results of both models reflect excellent performance and accuracy of around 99%. So it can be concluded that both CNN models, ResNet18 and ResNet50, have excellent performance in classifying garbage images.