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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
Wardaya, Imanuel Puspa
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Computer Science & IT Electrical & Electronics Engineering

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Penerapan Metode JST Menggunakan Fitur GLCM pada Identifikasi Penyakit Tumbuhan Stroberi Wardaya, Imanuel Puspa; Hermawan, Arief
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 12, No 3: Desember 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v12i3.1606

Abstract

In recent years, strawberry farmers have experienced crop failures caused by diseases in strawberry plants. The lack of knowledge of strawberry farmers about the signs of strawberry plant disease results in more severe crop failure. This research aims to create a strawberry plant disease identification system with the artificial neural network method and compare the accuracy level of the application of the artificial neural network method to the strawberry plant disease detection system. One of the methods in artificial neural networks that can be used to identify strawberry plant diseases is backpropagation algorithm. The research was conducted by comparing the activation used between ReLU, Sigmoid, and Tanh. The best accuracy was obtained using ReLU activation with an accuracy of 83.74% and a model evaluation accuracy of 50%. This system can be used by strawberry farmers to identify diseases quickly so that they can anticipate crop failure due to disease attacks.Keywords: Artificial Neural Network; Backpropagation; Grey Level Co-occurrence Matrix; Strawberry Plant Disease AbstrakDalam beberapa tahun terakhir, petani buah stroberi mengalami gagal panen yang diakibatkan oleh penyakit pada tumbuhan stroberi. Kurangnya pengetahuan petani buah stroberi tentang tanda penyakit tumbuhan stroberi mengakibatkan gagal panen yang lebih parah. Penelitian ini bertujuan untuk menciptakan sistem identifikasi penyakit tumbuhan stroberi dengan metode jaringan syaraf tiruan dan membandingkan tingkat akurasi penerapan metode jaringan syaraf tiruan terhadap sistem pendeteksi penyakit tumbuhan stroberi. Salah satu metode dalam jaringan syaraf tiruan yang dapat digunakan untuk mengidentifikasi penyakit tumbuhan stroberi adalah algoritma backpropagation. Penelitian dilakukan dengan membandingkan aktivasi yang digunakan antara ReLU, Sigmoid, dan Tanh. Akurasi terbaik didapatkan menggunakan aktivasi ReLU dengan akurasi sebesar 83,74% dan akurasi evaluasi model sebesar 50%. Sistem ini dapat digunakan petani tanaman stroberi untuk mengidentifikasi penyakit dengan cepat sehingga dapat mengantisipasi terjadinya gagal panen akibat serangan penyakit. 
Strawberry Fruit Disease Identification Using Digital Image Processing Using GLCM With Artificial Neural Network Method Wardaya, Imanuel Puspa; Hermawan, Arief
Telematika Vol 21 No 1 (2024): Edisi Pertama 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i1.9861

Abstract

Purpose: This research aims to identify strawberry fruit diseases using digital image processing using GLCM with the backpropagation artificial neural network method.Design/methodology/approach: Using images that have been preprocessed grayscale, crop, and resize and then processed using GLCM for traning using backpropagation artificial neural networks.Findings/result: Based on 250 image data that is processed by GLCM and classified using a backpropagation artificial neural network, it can be concluded that the best accuracy rate is obtained from ReLU activation with a traning data accuracy value of 95% and test data accuracy of 54%.Originality/value/state of the art: This research uses a combination of primary data with kaggle data by using a comparison of several experiments by changing the loss, optimizer and activation parameters.
Co-Authors Arief Hermawan