Open AccessKlasifikasi Belimbing Menggunakan Naïve Bayes Berdasarkan Fitur Warna RGB
Author(s) -
Fuzy Yustika Manik,
Kana Saputra Saragih
Publication year - 2017
Publication title -
indonesian journal of computing and cybernetics systems
Language(s) - English
Resource type - Journals
eISSN - 2460-7258
pISSN - 1978-1520
DOI - 10.22146/ijccs.17838
Subject(s) - rgb color model , artificial intelligence , feature extraction , pattern recognition (psychology) , computer science , naive bayes classifier , computer vision , feature (linguistics) , sorting , mathematics , support vector machine , philosophy , linguistics , programming language
Post harvest issues on star fruit are produced on a large scale or industry is sorting. Currently, star fruit classified by rind color analysis visually human eye. This method does not effective and inefficient. The research aims to classify the starfruit sweetness level by using image processing techniques. Features extraction used is the value of Red, Green and Blue (RGB) to obtain the characteristics of the color image. Then the feature extraction results used to classify the star fruit with Naïve Bayes method. Starfruit image data used 120 consisting of 90 training data and 30 testing data. The results showed the classification accuracy using RGB feature extraction by 80%. The use of RGB as the color feature extraction can not be used entirely as a feature of the image extraction of star fruit.