@thesis{thesis,
		author={LEWAR  Yovita Sili Tepo},
		title ={Klasifikasi Citra Warna Bunga Euphorbia Milli Menggunakan Mesin Learning},
		year={2024},
		url={http://repository.unwira.ac.id/16183/},
		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.}
		}