Klasifikasi Penyakit Pada Daun Kopi Robusta Menggunakan Arsitektur AlexNet dan Xception dengan Metode Convolutional Neural Network

Diseases on the leaves of robusta coffee plants can have a significant impact on the growth and yield of robusta coffee plants. The leaves of the robusta coffee plant are susceptible to various types of diseases caused by fungi, bacteria or insects with symptoms such as brown, yellow or black patches and discoloration on the surface of the leaves of the robusta coffee plant. Early detection of diseases in robusta coffee leaf plants is very important to obtain effective control to maintain plant health. In this study, a disease classification model on the leaves of robusta coffee plants was made using the Convolutional Neural Network (CNN) architecture. The architecture used in this study is AlexNet and Xception. In this study, a dataset of images of robusta coffee leaves obtained through direct observation of robusta coffee plantations in Temanggung Regency was used. The number of datasets used was 1400 data which was divided into 4 classes, namely healthy, root down, leaf rust and red spider mites. The CNN model was tested by setting parameters consisting of batch size, drop out, learning rate, optimizer and the number of epochs that varied 35, 50 and 100. The results of this study show that the AlexNet architecture model with 50 epoch tests obtains the best accuracy of 98.57% and the Xception architecture obtains an accuracy of 100% in each epoch test. Overall, the use of AlexNet and Xception architectures is very effective in classifying diseases in robusta coffee leaves, but the Xception architecture is superior in the ability to classify complex datasets and higher accuracy.