iThenticate Article of CLASSIFICATION OF DISEASES AND PESTS OF MAIZE USING MULTINOMIAL LOGISTIC REGRESSION BASED ON RESAMPLING TECHNIQUE OF K-FOLD CROSS-VALIDATION

Resti, Yulia (2014) iThenticate Article of CLASSIFICATION OF DISEASES AND PESTS OF MAIZE USING MULTINOMIAL LOGISTIC REGRESSION BASED ON RESAMPLING TECHNIQUE OF K-FOLD CROSS-VALIDATION. FMIPA Universitas Sriwijaya.

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Abstract

Some of the obstacles in the cultivation of maize that cause low productivity of maize yields are diseases and pests. Early detection of maize diseases and pests is expected to reduce farmer losses. A system for the early detection of diseases and pests can be created by classifying them based on digital images. This study aimed to classify maize diseases and pests using multinomial logistic regression. The model and testing resampling were based on the resampling technique of k-fold cross-validation. The research data was obtained from the RGB color feature extraction process for each object in each class of diseases and pests of corn. The results showed that the classification into seven classes using five folds had an accuracy rate of 99.85%, macro precision of 98.59%, and macro recall of 98.15%.

Item Type:	Other
Subjects:	#3 Repository of Lecturer Academic Credit Systems (TPAK) > Results of Ithenticate Plagiarism and Similarity Checker
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Mr. Irsyadi Yani, S.T., M.Eng., Ph.D.
Date Deposited:	28 Apr 2023 23:22
Last Modified:	28 Apr 2023 23:22
URI:	http://repository.unsri.ac.id/id/eprint/97934

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