No abstract

Produk pertanian kentang menjadi sangat penting karena termasuk makanan utama bagi manusia. Kentang memiliki kandungan karbohidrat yang menjadikanya sebagai makanan utama. Dalam mengelola pertanian kentang ini tentu memiliki beberapa kendala diantaranya adalah penyakit yang menyerang pada daun kentang yang jika dibiarkan akan menghasilkan produksi yang buruk atau bahkan gagal panen. Late blight dan early blight adalah penyakit yang sering ditemui pada daun kentang. Penyakit ini memiliki gejala masing-masing sehingga para petani dapat melakukan pencegahan jika melihat gejala pada daun kentang, tetapi langkah ini memliki kelemahan yaitu proses identifikasi yang lama, dan jika penanganan pada penyakit daun ini sangat lambat akan mengakibatkan penambahan biaya perawatan. Dengan memanfaatkan teknologi yaitu berupa pengolahan citra digital maka hal ini bisa diatasi, jadi pada penelitian ini akan mengusulkan metode yang tepat dalam mendeteksi penyakit pada daun kentang. Klasifikasi akan dilakukan dengan tiga kelas berupa daun sehat, early blight, dan late blight menggunakan metode Deep Learning mengguanakan arsitektur Convolutional Neural Network (CNN). Hasil pada peneltian ini dianggap baik karena pada epoch ke 10 dengan batch size 20 menghasilkan training akurasi 95% dan validation accuracy 94%.Kata Kunci—Penyakit daun kentang, late blight, early blight, identifikasi, CNNPotato agricultural products are essential because they are the leading food. Potatoes have carbohydrate content, which makes them the leading food for humans. But in carrying out this potato farming certainly has several obstacles, including the disease that attacks the potato leaves which if left unchecked will result in poor production or even crop failure. late blight and early blight are diseases that are often found in potato leaves. This disease has its own symptoms so that farmers can take precautions if they see symptoms on potato leaves, but this step has a weakness that is a long identification process, and if the handling of this leaf disease is very slow will result in additional maintenance costs. By utilizing technology in the form of digital image processing, this can be overcome, so this research will propose an appropriate method in detecting diseases in the leaves of potato plants. Classification will be carried out with three classes in the form of healthy leaves, early blight, and late blight using the Convolutional Neural Network (CNN) algorithm. The results of this research are considered good because on the 10th epoch with batch size 20 produces 95% accuracy training and 94% validation accuracy.Keywords—Potato leaf disease, late blight, early blight, identification, CNN

Potatoes are a plant that has many benefits for human life. The potato plant has a problem, namely a disease that attacks the leaves. Disease on potato leaves that is often encountered is early blight and late blight. Image processing is a method that can be used to assist farmers in identifying potato leaf disease by utilizing leaf images. Image processing method development has been done a lot, one of which is by using the Convolutional Neural Network (CNN) algorithm. The CNN method is a good image classification algorithm because its layer architecture can extract leaf image features in depth, however, determining a good CNN architectural model requires a lot of data. CNN architecture will become overfitting if it uses less data, where the classification model has high accuracy on training data but the accuracy becomes poor on test data or new data. This research utilizes the Transfer Learning method to avoid an overfit model when the data used is not ideal or too little. Transfer Learning is a method that uses the CNN architecture that has been trained by other data previously which is then used for image classification on the new data. The purpose of this research was to use the Transfer Learning method on CNN architecture to classify potato leaf images in identifying potato leaf disease. This research compares the Transfer Learning method used to find the best method. The results of the experiments in this research indicate that the Transfer Learning VGG-16 method has the best classification performance results, this method produces the highest accuracy value of 95%.