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Fifi Febrianti Usman
Fakultas Ilmu Komputer UMI
Computer Science & IT

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Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Convolutional Neural Network dan K-Nearest Neighbor Fifi Febrianti Usman; Purnawansyah; Herdianti Darwis; Erick Irawadi Alwi
Computer Science Research and Its Development Journal Vol. 15 No. 3 (2023): October 2023
Publisher : LPPM Universitas Potensi Utama

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Abstract

The potential for yield loss due to shallot plant disease is the main trigger that can reduce agricultural productivity. Pest and disease attacks can be minimized and overcome quickly if farmers are able to classify the types of diseases that attack plants based on the characteristics and symptoms that appear. This study aims to classify shallot plant diseases, namely purple spotting and moles with a total of 320 datasets using Hue Saturation Value color feature extraction using the K-Nearest Neighbor (Euclidean Distance) and Convolutional Neural Network methods. Based on the results of the study, the accuracy, f1-score was 94% and precision, recal was 97%, 91% in purple spot disease while in moler disease it was 94% in accuracy, precision, recall, and f1-score in HSV and KNN classifications. Classification using HSV and CNN yielded high scores in accuracy, precision, recall, and f1-score with a value of 100% in both purple spot and moler shallot leaf diseases. Classification using deep learning CNN obtains very good accuracy, precision, recall and f1-score, namely 100%. With this description, the classification of shallot plant diseases using HSV and CNN, and CNN deep learning are stated to be able to classify shallot plant diseases, namely purple spotting and moles effectively and accurately.
Co-Authors Erick Irawadi Alwi Herdianti Darwis Purnawansyah