2020
DOI: 10.26554/sti.2020.5.3.75-78
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Improved the Cans Waste Classification Rate of Naïve Bayes using Fuzzy Approach

Abstract: Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the 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 … Show more

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Cited by 4 publications
(7 citation statements)
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“…Using a combination of linear and triangular fuzzy membership functions in discretizing the predictor variable also succeeded in increasing the performance of the naïve Bayes model in predicting the type of cans based on digital images [11]. Each predictor variable is discretized into three categories.…”
Section: Related Workmentioning
confidence: 99%
See 4 more Smart Citations
“…Using a combination of linear and triangular fuzzy membership functions in discretizing the predictor variable also succeeded in increasing the performance of the naïve Bayes model in predicting the type of cans based on digital images [11]. Each predictor variable is discretized into three categories.…”
Section: Related Workmentioning
confidence: 99%
“…The selection of fuzzy membership functions representing linguistic terms in fuzzy discretization is subjective [6,9,11]. Several fuzzy membership functions used in this work are defined in Equations ( 7)-( 12) [9,39].…”
Section: Type Of Fuzzy Membership Functionmentioning
confidence: 99%
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