Miftahuddin, Yusup and Umaroh, Sofia and YAMANI, ADLEO MALIK (2022) Peningkatan Random Forest dengan menerapkan GLCM (Gray Level Co-Occurence Matrix) pada Klasifikasi Leaf Blast Tumbuhan Padi. MIND (Multimedia Artificial Intelligent Networking Database) Journal, 7 (1). pp. 37-50. ISSN cetak 2528-0015 dan ISSN elektronik 2528-0902

Text (Peningkatan Random Forest dengan menerapkan GLCM (Gray Level Co-Occurence Matrix) pada Klasifikasi Leaf Blast Tumbuhan Padi) Peningkatan Random Forest dengan menerapkan GLCM (Gray Level Co-Occurence Matrix) pada Klasifikasi Leaf Blast Tumbuhan Padi.pdf Download (1MB)

Abstract

Leaf blast is a disease caused by a fungus called Pyricularia Grisea which can infect rice leaves and cause disease symptoms such as brown rhombus-shaped spots that can cause plant death. The level of spread of leaf blast disease has spread to Indonesia, namely in rice production centers. The research was conducted to identify Rice Leaf with GLCM feature extraction and classification by applying the Random Forest method. The number of test data was 200 consisting of 100 data of healthy rice leaves and 100 data of rice leaves with leaf blast disease. The study tested the success of identification of leaf blast disease and not leaf blast disease. The tests were carried out with various schemes, namely 40 test data, 80 test data, 120 test data, 160 test data and 200 test data. The test resulted in the optimal accuracy value on the 200 test data of 65%, recall 65%, precision 64% and F-measure 65% with an average testing time of Random Forest classification of 0.3522s Keywords: Leaf blast, Random Forest, Gray-level Cooncurrence Matrix, GLCM

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