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All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA
Tami Dayatmi
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Computer Science & IT

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Identifikasi Bunga Kertas (Bougenville) Berdasarkan Warna dengan Metode K-Nearest Neighbor (KNN) Tami Dayatmi; Nurhayati Nurhayati; Husnul Khair
ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Vol 5, No 2 (2021): November 2021
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v5i2.10506

Abstract

In this study, the process of determining the type of data was carried out. Bougenville flowers or commonly referred to as paper flowers are ornamental plants whose existence is quite popular among the public and is widespread in various regions in Indonesia. The data collected for this research are image files in Portable Network Graphics (PNG) format which were obtained using a digital camera. The image that becomes the input is the image of Bunga Bougenville. The sample data used are 3 data on each image sample, with each having 3 attributes, namely red, green, blue. The dataset is the result of image extraction which will be a data source for fruit image classification using the K-Nearest Neighbor method. As for the results of testing the K-Nearest Neighbor method in data classification. The author's test uses variations in the K value of K-Nearest Neighbor 3,4,5,6,7,8,9. Has a very good percentage of accuracy compared to only K-NN. The test results show the K-Nearest Neighbor method in data classification has a good percentage accuracy when using random data. The percentage of variation in the value of K K-Nearest Neighbor 3,4,5,6,7,8,9 has a percentage of 100%.  Keywords : K-Nearest Neighbor, Paper Flowers (Bougenville)
Co-Authors Khair, Husnul Nurhayati Nurhayati