Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Kerapihan Ruangan

This study implements a Convolutional NeurallNetwork (CNN) to classify room images into two categories: messy and clean. The model utilizes VGG16 as a feature extractor, followed by fully connected layers and a sigmoid activation function in the output layer. This approach is simpler compared to the softmax scheme, which is commonly used for multi-class classification. The dataset was augmented to enhance the model's generalization. Evaluation results show a validation accuracy of 98,63%, indicating the effectiveness of the model in binary classification tasks