Klasifikasi Penyakit Daun pada Tanaman Padi dan Rekomendasi Penanganan menggunakan Metode Convolutional Neural Network Berbasis Android

Bahrudin, Ayub (2025) Klasifikasi Penyakit Daun pada Tanaman Padi dan Rekomendasi Penanganan menggunakan Metode Convolutional Neural Network Berbasis Android. Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

The rice leaf disease identification system is an important aspect of maintaining the quality of rice production. This study aims to develop an Android-based application, Harvest-Guard, which can help farmers identify rice leaf diseases and provide recommendations for appropriate treatment. This application uses imagebased Deep Learning technology with the Convolutional Neural Network (CNN) algorithm. This method has been proven effective in classifying rice leaf diseases with a very high level of accuracy. During testing, removing duplication, balancing data, and utilizing data augmentation techniques to the training model successfully improved model performance, achieving 99% accuracy in each disease class. Model evaluation was carried out using the Confusion Matrix and Classification Report to ensure each class's precision, recall, and f1-score of each class. The test data used was 10% of the total 4328 image data for four classes of rice leaf diseases. In addition, the user satisfaction test showed excellent results, with an average percentage of 88% from five respondents. Testing using black box testing also produced results by the nine features of the Harvest-Guard application. With Harvest-Guard, farmers are expected to more easily detect and handle rice leaf diseases to improve the quality of the harvest and overall food security.

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