Penerapan Algoritma Random Forest dengan Kombinasi Ekstraksi Fitur Untuk Klasifikasi Penyakit Daun Tomat

 (*)Umi Khultsum Mail (STMIK Nusa Mandiri, Jakarta, Indonesia)
 Agus Subekti (Lembaga Ilmu Pengetahuan Indonesia, Indonesia)

(*) Corresponding Author

Submitted: December 3, 2020; Published: January 22, 2021



The tomato plant is widely consumed by the community and is widely cultivated by farmers. Tomato plants are susceptible to disease attacks. Plant diseases cause a decrease in the quality and quantity of crops or agricultural produce. The idea of the 4.0 agricultural revolution emerged as a result of the 4.0 industrial revolution. Farmers are not ready to face increasingly rapid technological advances. It is important to identify the disease in tomato leaves correctly in the efficiency of disease management for efforts to control so that disease in tomato leaves does not develop. The main objective of the proposed method is to develop a technique for identifying foliar diseases in tomato plants by increasing the classification accuracy. The novelty of this research is a combination of several feature extractions to improve classification accuracy. The features used are the color feature, the Hu-Moment feature, and the firur haralick. In the classification process, the Random Forest algorithm and other classification algorithms are applied for comparison. In this study, the Random Forest method and the combination of extraction features have shown an increase in accuracy, the accuracy obtained is 96%.


Tomato Plant Diseases; Classification; Color Features; Hu-Moment Features; Haralick Features

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