Klasifikasi Penyakit Daun Padi menggunakan Random Forest dan Color Histogram - Sarifah Agustiani; Yoseph Tajul Arifin; Agus Junaidi; Siti Khotimatul Wildah; Ali Mustopa (Jurnal Komputasi, 2022)
Indonesia is an agrarian country, which is a sector that plays an important role most of the Indonesian population makes agriculture the main focus, but the function of rice fields into housing or industry has resulted in a decrease in rice production, in addition to pests, diseases, unfavorable weather, Irrigation is not smooth resulting in less than the maximum yield. For this reason, it is necessary to have technology that can implement the process of detecting rice leaf disease in order to provide information to farmers about rice leaf damage. The most modern approach today can be done with machine learning or deep learning by using various algorithms to improve recognition and accuracy in the detection and diagnosis of plant diseases. Based on this, this study aims to propose a method of classifying rice leaf diseases in order to provide information to farmers about rice leaves which are expected to reduce the disease by detecting the disease early so as to increase rice production. In this study, the classification process is carried out using the augmented image, then the Color Histogram feature extraction method is applied, and the classification is carried out using the Random Forest algorithm. In addition, this study also conducted several comparisons, including feature extraction and yahoo to get the results, and the highest results reached 99.65% of the proposed method.