Klasifikasi Citra Warna Bunga Euphorbia Milli Menggunakan Mesin Learning
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Institusion
Universitas Katolik Widya Mandira Kupang
Author
LEWAR, Yovita Sili Tepo
Subject
TN Mining engineering. Metallurgy
Datestamp
2024-05-27 01:25:07
Abstract :
Euphorbia milli flowers are ornamental plants that are easy to find in home gardens and public places. In this research, the image of the Euphorbia milli flower is an interesting object to classify by comparing two methods, namely K-NN and Naïve Bayes. The purpose of comparing the two methods is to determine the level of accuracy of the classification results of the two methods. The dataset with a total of 2832 images was divided into four samples consisting of 708 red images, 708 yellow images, 708 pink images, and 708 white images. The methods used to calcify the image of the Euphorbia milli flower are the K-NN method and the Naïve Bayes method. The results of the classification using the K-NN method obtained an accuracy level of 97.2% and the results of the Naïve Bayes classification obtained an accuracy level of 76.2%. These accuracy results are obtained through the results of the confusion matrix performance in the Orange Data Mining application through a classification process, namely image import, image viewer, image embedding, data table, and test and score. The K-NN method has a higher level of accuracy than the Naïve Bayes method. Keywords: Classification, Flower Image, K-NN and Naïve Bayes.
Euphorbia milli flowers are ornamental plants that are easy to find in home gardens and public places. In this research, the image of the Euphorbia milli flower is an interesting object to classify by comparing two methods, namely K-NN and Naïve Bayes. The purpose of comparing the two methods is to determine the level of accuracy of the classification results of the two methods. The dataset with a total of 2832 images was divided into four samples consisting of 708 red images, 708 yellow images, 708 pink images, and 708 white images. The methods used to calcify the image of the Euphorbia milli flower are the K-NN method and the Naïve Bayes method. The results of the classification using the K-NN method obtained an accuracy level of 97.2% and the results of the Naïve Bayes classification obtained an accuracy level of 76.2%. These accuracy results are obtained through the results of the confusion matrix performance in the Orange Data Mining application through a classification process, namely image import, image viewer, image embedding, data table, and test and score. The K-NN method has a higher level of accuracy than the Naïve Bayes method. Keywords: Classification, Flower Image, K-NN and Naïve Bayes.
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