Ripeness Identification of Chayote Fruits using HSI and LBP Feature Extraction with KNN Classification


Siska Anraeni(1*); Erika Riski Melani(2); Herman Herman(3);

(1) Universitas Muslim Indonesia
(2) Universitas Muslim Indonesia
(3) Universitas Muslim Indonesia
(*) Corresponding Author

  

Abstract


This study aims to build a system to identify the maturity level of chayote so that the determination of maturity can be done easily and without damaging the quality of the chayote by utilizing digital image processing technology using Hue Saturation Intensity color feature extraction and texture feature extraction of Local Binary Pattern with K-classification. Nearest Neighbor so that the process of identifying the maturity level of chayote will be easier and more effective. This study uses 100 image datasets and is carried out by taking photos of chayote. The stages in this study include the input of chayote images followed by the image pre-processing stage, then feature extraction which is divided into three scenarios, namely HSI feature extraction, LBP and a combination of the two feature extractions. The next stage is to classify objects that are closest to the object being tested using the KNN method. The test results by determining the value of K in the KNN classification method show that the use of the Chebyshev distance calculation model in HSI feature extraction with K = 5 is a test that has the best accuracy of 90%.


Keywords


hue saturation intensity; local binary patern; K-Nearest Neighbor; chayote fruits

  
     

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