Deteksi Penyakit Daun pada Tanaman Padi Menggunakan Algoritma Decision Tree, Random Forest, Naïve Bayes, SVM dan KNN
Abstract
Agriculture as one of the industrial sectors has become a part of the work that supplies people's basic food needs, such as food crops, rice is a food that is susceptible to pests. Identifying the host of pests is a vital first step toward promoting success in its control. The pest of the rice plant can pose a challenge for farmers to increase production. Because such pests can damage the crops to the point of failure. It is therefore necessary to assess the classification of rice leaf pests for accuracy by using a variety of algorithm-based methods of decision tree, random forest, naive bayes, SVM and KNN in the hope that farmers will soon discover the type of rice pests and their ferocity levels. And so it is expected to be able to handle eve properly, lest the damage and failure of the harvest, by using datassets Rice leaf diseases diseases diseases to detect and classification rice diseases. This datasset has three classes The underlying diseases: leaf rot, chocolate patches, and fire leaves produce 40 images each with JPG in their format. In comparison to the five possible methods of the algorithm, the three types of models are the overfit models (Random Forest, Decission Tree dan Naive Bayes), Model Underfit (SVM) dan Good Models (KNN). So the fifth prime method, however, is the KNN method with an accuracy of 87%, because it is consistent with both evaluations. KNN has no evidence of overfiting problems because it consistently performs well on train data and test data.
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DOI: https://doi.org/10.30743/infotekjar.v5i1.2934
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