Deteksi Kematangan Buah Nanas Dengan Fitur Citra Kulit Menggunakan Metode YCbCr

ROA, Thresia Delvina (2023) Deteksi Kematangan Buah Nanas Dengan Fitur Citra Kulit Menggunakan Metode YCbCr. Undergraduate thesis, Universitas Katolik Widya Mandira Kupang.

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Abstract

Pineapple is a tropical plant with edible fruit and the most important plant economically and family bromeliaceae. The quality of ripeness is very influential in obtaining standard fruit quality, but there are often problems or mistakes in harvesting pineapples because farmers still determine the maturity level of pineapples manually. conventional (manually) using vision / human power. The purpose of this study was to find out how accurate detection of ripeness of pineapple using YCbCr color feature extraction with the K-Nearest Neighbor (KNN) algorithm and classified into several sections, namely, data collection techniques, needs analysis, design and training then testing. The image sample data used in this study amounted to 480 images consisting of raw Bogor pineapple, ripe Bogor pineapple, ripe local pineapple and raw local pineapple, which will then be divided into training data and test data. The training data sample is 400 images divided into 100 ripe Bogor pineapple images, 100 raw Bogor pineapple images, 100 ripe local pineapple images and 100 raw local pineapple images. Meanwhile, the test data sample consisted of 80 images divided into 20 images of ripe Bogor pineapple, 20 images of raw Bogor pineapple, 20 images of ripe local pineapple and 20 images of raw local pineapple. Characteristic analysis of pineapple skin features using Red, Green and Blue (RGB) colors which will be converted into the YCbCr u color space For the color feature extraction process YCbCr was successfully applied with an accuracy value for training for pineapple species of 76%, maturity of 98.75% and testing for type pineapple 76.25% and 100% maturity.

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