Classification of Roasting Rates in Coffee Beans By Digital Image Processing Using The Naive Bayes Classifier (NBC) Method

Hidayat, M. Mahaputra and Prasetyo, Eko and Wicaksono, Bambang Teguh (2023) Classification of Roasting Rates in Coffee Beans By Digital Image Processing Using The Naive Bayes Classifier (NBC) Method. JURNAL IPTEK MEDIA KOMUNIKASI TEKNOLOGI, 27 (1). pp. 23-30. ISSN 1411- 7010

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Abstract

Coffee is the bean of the coffee plant and is the source of coffee drinks. Coffee beans must pass through the coffee roasting stage or also called coffee roasting, from this stage of the process the coffee will be roasted and this process also has its own level. At this stage of the roasting process coffee shop business people often do not know this process, then in this case used Local Binary Pattern (LBP) method. LBP is a simple and very efficient texture operator by labeling pixels by doing thresholding on each pixel neighbors and considers the result as a binary number. This method is used to obtain the texture ektrasi of an image. While for the classification method using the Naive Bayes Classifier (NBC) method. Naive Bayes is a simple probabilistic classifier that calculates a set of probabilities by summing the frequencies and combinations of values from a given dataset. The algorithm uses Bayes' theorem and assumes all the independent or non-interdependent attributes given by the values on the class variables. From the test results by comparing training data and testing data obtained an accuracy rate of 81%. For an image-based developed system with display recognition difficulties, this performance is good.

Item Type:	Article
Uncontrolled Keywords:	Coffee beans, Coffee Bean roasting, Local Binary Pattern, Naive Bayes Classifier, Classification System.
Subjects:	Technology
Divisions:	Faculty of Engineering
Depositing User:	Perpus Ubhara Surabaya
Date Deposited:	04 Jun 2024 06:52
Last Modified:	04 Jun 2024 06:52
URI:	http://eprints.ubhara.ac.id/id/eprint/2479

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