Klasifikasi Penyakit Daun Jagung Menggunakan Lightweight Convolutional Neural Network - Hermawan, Andri Lesmana (Jurusan Informatika STTR Cepu, 2023)
Deep Neural Networks have achieved great success in many tasks and research especially in pattern recognition. However, high computing and large memory requirements prevent them from being applied to limited devices and require large costs. And Imbalance of data in Machine Learning refers to the unequal distribution of classes in a data set. This problem is often encountered in classification tasks where the distribution of classes within a given data set is not uniform. Given these problems, the researchers proposed the Light Neural Network method and added the Resampling method to Corn Leaf Disease data, to ease the computational process and balance the data. Researchers use several methods in machine learning which involve the use of knowledge that has been learned by existing models (Transfer Learning), including: MobileNet, EfficientNetB1 and Exception, as well as conducting comparisons between models. These models use the Depthwise Separable Convolution technique to reduce the number of parameters and convolution operations that need to be performed, thereby reducing the computational requirements and model complexity. The results of light neural network research and the combination of Resampling, the highest Transfer Learning model of 95% obtained from the EfficientNetB1 model than other Transfer learning models.