Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network

Mohtar, Khoiruddin (2021) Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

	Text Cover.pdf Download (459kB)
	Text Abstract.pdf Download (55kB)
	Text Abstrak.pdf Download (57kB)
	Text BAB I.pdf Download (85kB)
	Text BAB II.pdf Download (465kB)
	Text BAB III.pdf Download (270kB)
	Text BAB IV.pdf Restricted to Registered users only Download (529kB) | Request a copy
	Text BAB V.pdf Download (57kB)
	Text Daftar Pustaka.pdf Download (142kB)
	Text Lampiran.pdf Restricted to Registered users only Download (156kB) | Request a copy

Abstract

Rice (Oryza sativa) is the third most common grain after corn and wheat in terms of output. Rice is a staple cuisine for 80 percent of Indonesians, especially in Southeast Asian countries. however, according to Mr. Supomo, the coordinator of the Agricultural Extension Agency of Cluwak sub-district, Pati district, there are still many farmers who are unsure about identifying the sort of disease that attacks rice plants, as well as controlling it, resulting in crop failure. As a result, it is critical to investigate the identification of rice pests and diseases. Using the Convolution Neural Network (CNN) approach, an automatic classification system to identify and predict plant diseases has been developed. A study titled Classification of Rice Leaf Diseases was undertaken by the author. The goal of using the CNN approach to determine the results of the classification of rice plant illnesses is to use the CNN method to determine the results of the classification of rice plant diseases. Bacterial leaf blight, Rice blast, and Rice tungro virus were among the rice leaf types classified in this study. There are 6000 datasets in all, with 80% of them being training data, 10% being validation data, and 10% being testing data. The accuracy of the results obtained for epochs 25, 50, 75, and 100 varies. The best training accuracy results come from epoch 100, which has a 98% accuracy rate, and testing with the Confusion Matrix has a 98% accuracy rate. In classifying rice leaf illnesses, the Convolutional Neural Network (CNN) technique has a 98% accuracy rate. Keyword: Rice, Leaf Disease, CNN

Actions (login required)

View Item