Garbage Image Classification Using Deep Learning: A Performance Comparison of InceptionResNetV2 vs ResNet50 - Rismiyati, Rismiyati; Situmeang, Axelliano Rafael; Khadijah, Khadijah; Endah, Sukmawati Nur (Informatika, Universitas Jenderal Soedirman, 2025)
Garbage problem is a worldwide problem. Efforts to address garbage problem have been performed in several aspect, including automatic garbage classification to support automatic garbage sortation in small scale. In the field of garbage classification, deep learning has been widely used because of its ability to learn feature and also to classify with high accuracy. Several promising architectures in deep learning such as ResNet50 and InceptionNet have been used for this classification task. InceptionResNet is introduced to combine the strength of both architectures. This research aims to classify Garbage Classification data set which consist of 15150 images from 12 classes by using InceptionResNetV2 architecture. In addition, experiment by using ResNet-50 is also performed to provide comparison of its performance. During experiment, Hyperparamater tuning was performed, namely the learning rate, dropout rate, and the number of neuron in the dense layer. The results show that InceptionResNetV2 outperform ResNet50 in all scenarios. This architecture is able to achieve highest accuracy of 97.54%. Even though the classification time is longer for InceptionResNetV2, this finding is able to prove the outstanding performance of InceptionResNetV2 in garbage classification. This study contributes to the field of garbage classification by introducing robust and better model for better classification.