Metode Convolutional Neural Network(CNN) Untuk Klasifikasi Tingkat Kesehatan Tanaman Lidah Buaya Berbasis Web - Nurlatifa, Nur Latifa (JATISI, 2024)
Deep Learning is part of the field of digital image processing. Convolutional Neural Network (CNN) is an advancement of the Multilayer Perceptron (MLP) designed to process two-dimensional data. CNN is included in the category of Deep Neural Networks due to its deep architecture and is widely applied to image data. The aim of this research is to determine the accuracy level of the CNN method in classifying the health level of aloe vera plants integrated into a web platform. This study builds a model using the Convolutional Neural Network (CNN) algorithm. Convolutional Neural Network is one of the effective algorithms in image processing. Images will go through processes of resizing, normalization, and data augmentation. The dataset used consists of aloe vera plants with a total of 3,495 image samples: 1,514 images for training data, 630 images for validation data, and 351 images for testing data. In the modeling process, there are four convolutional layers and four max pooling layers followed by two fully connected layers. The training results of the built model have an average accuracy of 92% and validation accuracy of 85%, while the model testing results achieved an average accuracy of 89%. The CNN method's results for classifying the health level of aloe vera plants are effective in predicting the health level of aloe vera plants using the website.