Prosiding Seminar Nasional Ilmu Komputer Universitas Semarang 2022
Klasifikasi Penyakit Tanaman Padi dari Citra Daun Menggunakan Inceptionv-3 pada Convolutional Neural Network
Dublin Core
Title
Prosiding Seminar Nasional Ilmu Komputer Universitas Semarang 2022
Klasifikasi Penyakit Tanaman Padi dari Citra Daun Menggunakan Inceptionv-3 pada Convolutional Neural Network
Klasifikasi Penyakit Tanaman Padi dari Citra Daun Menggunakan Inceptionv-3 pada Convolutional Neural Network
Subject
Penyakit daun padi, convolutional neural network, klasifikasi citra, inception v3
ice leaf disease, convolutional neural network, image classification, inception v3
ice leaf disease, convolutional neural network, image classification, inception v3
Description
Padi merupakan makanan pokok orang Asia, khususnya di Indonesia. Angka konsumsi nasi di Indonesia sangat tinggi, sehingga petani harus memproduksi beras dengan angka yang besar juga. Penyakit pada tanaman, khususnya tanaman padi, menyebabkan kerugian produksi dan ekonomi yang besar. Penelitian ini memiliki tujuan untuk klasifikasi penyakit tanaman padi berdasarkan citra daun. Daun padi memiliki penampang yang luas dibandingkan batang atau akarnya, sehingga mudah untuk mendeteksi secara dini penyakit pada tanaman padi. Metode yang digunakan untuk klasifikasi penyakit pada daun padi adalah dengan Convolutional Neural Network (CNN) menggunakan aristektur Inception-V3. Dataset berupa citra daun padi yang terkena penyakit dikumpulkan dari kaggle dan memiliki 3 kelas, yaitu Brown Spot dengan 433 data citra, Bacterial Leaf Blight dengan 426 data citra, dan Leaf Smut dengan 435 data citra. Data dibagi menjadi 3 set data (training, validasi, dan testing) dengan perbandingan 70:20:10. Hasil performa dievaluasi dengan confusion matrix. Dari hasil pelatihan dan uji model didapatkan hasil akurasi 89%. Pada pengujian hasil akurasi pada masing-masing kelas sebesar 100%%, 85%, dan 82%.
Rice is the staple food of Asian people, especially in Indonesia. The rice consumption rate in Indonesia is very high, so farmers have to produce rice in large numbers as well. Diseases in plants, especially rice plants cause large production and economic losses. This study aims to classify rice plant diseases based on leaf image. Rice leaves have a wider cross-section than the stem or roots, making it easy to detect early disease in rice plants. The method used for disease classification in rice leaves is the Convolutional Neural Network (CNN) using the Inception-V3 architecture. The dataset in the form of images of diseased rice leaves was collected from kaggle and has 3 classes, namely Brown Spot with 433 image data, Bacterial Leaf Blight with 426 image data, and Leaf Smut with 435 image data. The data is divided into 3 data sets (training, validation, and testing) with a ratio of 70:20:10. Performance results are evaluated by confusion matrix. From the results of training and model testing, the results obtained accuracy of 89%. In testing the results of accuracy in each class of 100%%, 85%, and 82%.
Rice is the staple food of Asian people, especially in Indonesia. The rice consumption rate in Indonesia is very high, so farmers have to produce rice in large numbers as well. Diseases in plants, especially rice plants cause large production and economic losses. This study aims to classify rice plant diseases based on leaf image. Rice leaves have a wider cross-section than the stem or roots, making it easy to detect early disease in rice plants. The method used for disease classification in rice leaves is the Convolutional Neural Network (CNN) using the Inception-V3 architecture. The dataset in the form of images of diseased rice leaves was collected from kaggle and has 3 classes, namely Brown Spot with 433 image data, Bacterial Leaf Blight with 426 image data, and Leaf Smut with 435 image data. The data is divided into 3 data sets (training, validation, and testing) with a ratio of 70:20:10. Performance results are evaluated by confusion matrix. From the results of training and model testing, the results obtained accuracy of 89%. In testing the results of accuracy in each class of 100%%, 85%, and 82%.
Creator
Fandyka Ariel Pradana, Muhammad Galih Rakasiwi
Publisher
Universitas Semarang
Date
19 Oktober 2022
Contributor
Sri Wahyuni
Rights
ISSN: 2614-1205
Format
PDF
Language
Indonesian
Type
Text
Coverage
Prosiding Seminar Nasional Ilmu Komputer Universitas Semarang 2022
Files
Citation
Fandyka Ariel Pradana, Muhammad Galih Rakasiwi, “Prosiding Seminar Nasional Ilmu Komputer Universitas Semarang 2022
Klasifikasi Penyakit Tanaman Padi dari Citra Daun Menggunakan Inceptionv-3 pada Convolutional Neural Network,” Repository Horizon University Indonesia, accessed April 3, 2025, https://repository.horizon.ac.id/items/show/3514.
Klasifikasi Penyakit Tanaman Padi dari Citra Daun Menggunakan Inceptionv-3 pada Convolutional Neural Network,” Repository Horizon University Indonesia, accessed April 3, 2025, https://repository.horizon.ac.id/items/show/3514.