Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN)

Abstract

Jambi Province is a producer of palm oil as a mainstay of commodities. However, the limited insight of farmers in Jambi to oil palm pests and diseases affects oil palm productivity. Meanwhile, knowing the types of pests and diseases in oil palm requires an expert, but access restrictions are a problem. This study offers a diagnosis of oil palm disease using the most popular concept in the field of artificial intelligence today. This method is deep learning. Various recent studies using CNN, say the results of image recognition accuracy are very good. The data used in this study came from oil palm image data from the Jambi Provincial Plantation Office. After the oil palm disease image data is trained, the training data model will be stored for the process of testing the oil palm disease diagnosis. The test evaluation is stored as a configuration matrix. So that it can be assessed how successful the system is to diagnose diseases in oil palm plants. From the testing, there were 2490 images of oil palm labeled with 11 disease categories. The highest accuracy results were 0.89 and the lowest was 0.83, and the average accuracy was 0.87. This shows that the results of the classification of oil palm images with CNN are quite good. These results can indicate the development of an automatic and mobile oil palm disease classification system to help farmers.

Identifikasi Penyakit pada Citra Daun Kentang Menggunakan Convolutional Neural Network (CNN)

“…Terdapat pada penelitian sebelumnya yang membahas tentang penerapan algoritma Convolutional Neural Network terhadap diagnosis penyakit sawit yang menguji sebanyak 2490 citra menghasilkan nilai akurasi tertinggi sebesar 89% [5]. Penelitian lain dilakukan dengan sedikit modifikasi yaitu dengan penerapan Convolutional Neural Network dengan arsitektur MobileNet pada identifikasi penyakit pada tanaman gambas menghasilkan nilai akurasi sebesar 90% [6].…”

Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN

“…Data preprocessing is performed before training. Its purpose is to convert raw data into prepared, structured, and useable data [21]. Details of preprocessing shown by Table 3.…”

Deteksi Penyakit Kulit Wajah Menggunakan Tensorflow Dengan Metode Convolutional Neural Network

“…Errissya Rasywir, dkk melakukan klasifikasi pada penyakit sawit yang menggunakan metode CNN (Convolutional Neural Network) dengan hasil 0,98 yang merupakan ketepatan terbesar dan 0,83 yang merupakan ketepatan terendah dengan ketepatan 0,87 sebagai rata-rata [4].…”