Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi

Dicki Irfansyah; Metty Mustikasari; Amat Suroso

doi:10.30591/jpit.v6i2.2802

Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi

Dicki Irfansyah, Metty Mustikasari, Amat Suroso

Abstract

Indonesia is the fourth largest coffee producing country in the world. However, when compared to 3 other countries, Indonesia's coffee production is still relatively small. Many factors cause this to happen, including the number of farmers' coffee trees that are attacked by diseases. If the handling of this disease is slow, then the disease in one tree can be transmitted to other trees. This causes a decrease in Indonesian coffee productivity. In this study, the author implemented the Alexnet Convolutional Neural Network (CNN) architecture using the MATLAB programming platform for the identification of diseases in coffee plants through images. The total number of datasets used is 300 data which is divided into 3 classes, namely health, rust and red spider mite. The training process involving 260 training data resulted in an accuracy of 69.44-80.56%. The network testing process using 40 test data resulted in an accuracy of 81.6%. Based on the results of the study, it can be said that the Alexnet architecture is accurate for the classification of leaf pests on coffee plants

Keywords

Klasifikasi Citra, MATLAB, Convolutional Neural Network, Arsitektur Alexnet, Tanaman Kopi

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DOI: https://doi.org/10.30591/jpit.v6i2.2802