iThenticate Article of Improved the Cans Waste Classification Rate of Naïve Bayes using Fuzzy Approach

Resti, Yulia (2020) iThenticate Article of Improved the Cans Waste Classification Rate of Naïve Bayes using Fuzzy Approach. FMIPA Universitas Sriwijaya.

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Abstract

Cans are one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for sorting system automation. This paper discusses the cans classification system based on digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 50.26 % to 85.19 % or an increase of 34.93 %, where the standard deviation decreased from 14.01 % to only 6.29 %. A decrease in the standard deviation of 7.72 % also indicates that this model is better than the ONB model.

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 23:17
Last Modified:	28 Apr 2023 23:17
URI:	http://repository.unsri.ac.id/id/eprint/97965

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