THE ENSEMBLE METHOD AND SCHEDULED LEARNING RATE TO IMPROVE ACCURACY IN CNN USING SGD OPTIMIZER

Indonesia is an agricultural country where most people work as farmers. As an agricultural country, Indonesia produces staple foods, such as rice, corn, sago, and fruits. This research uses the Convolutional Neural Network (CNN), one of the popular algorithms in Deep Learning to classify two varieties of fruit using Stochastic Gradient Descent (SGD) optimizer. The data used in this research is the primary data collected using a smartphone camera. The data are 400 images of two fruit varieties, Mango, and Avocado. The main research objective is to obtain the highest accuracy by modifying the classifier model and learning rate. The model modification, this research uses an ensemble system while in learning rate using an exponential scheduled learning rate. The result shows that the accuracy of the ensemble system is 0.99, the scheduled learning rate is 0.97, while without modifications is 0.53, respectively. However, when using the SGD optimizer to train CNN, it is advised to use a predefined learning rate. A shorter training period with sufficient model accuracy and practicality supports the scheduled learning rate.