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Analysis K-Nearest Neighbor Method in Classification of Vegetable Quality Based on Color

Journal of Applied Engineering and Technological Science (JAETS) · Vol. 3 No. 2 (2022) · hlm. 126–132

DOI
10.37385/jaets.v3i2.763
ISSN
2715-6079, 2715-6087
Terbit
2022-06-24
Penerbit
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Bahasa
en

Abstrak

In this research, the process of applying the K-Nearst Neighbor (KNN) method will be carried out, which is a classification method for a collection of data based on the majority of categories and the goal is to classify new objects based on attributes and sample samples from training data. So that the desired output target is close to the accuracy in conducting learning testing. The results of the test of the K-Nearest Neighbor method. It can be seen that from the K values??of 1 to 10, the percentage of the results of the analysis of the K-NN method is higher than the results of the analysis of the K-NN method. And from the K value that has been tested, the K 2 value and the K 9 value have the largest percentage so that the accuracy is also more precise. As for the results of testing the K-Nearest Neighbor method in data classification. As for the author's test using a variation of 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%.

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  • Politeknik Negeri Medan
  • Universitas Pembangunan Panca Budi Medan
  • Universitas Sumatera Utara
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Rekan penulisOrganisasiArtikel bersama
Yuyun Yusnida LasePoliteknik Negeri Medan1
Arif Ridho LubisPoliteknik Negeri Medan1
Santi PrayudaniPoliteknik Negeri Medan1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.yrpipku.com/index.php/jaets/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Analysis K-Nearest Neighbor Method in Classification of Vegetable Quality Based on Color</dc:title>
	<dc:creator xml:lang="en">Putra, Purwa Hasan</dc:creator>
	<dc:creator xml:lang="en">Novelan, Muhammad Syahputra</dc:creator>
	<dc:creator xml:lang="en">Rizki, Muhammad</dc:creator>
	<dc:subject xml:lang="en">Machine Learning</dc:subject>
	<dc:subject xml:lang="en">Data Classification</dc:subject>
	<dc:subject xml:lang="en">Vegetable Quality</dc:subject>
	<dc:description xml:lang="en">In this research, the process of applying the K-Nearst Neighbor (KNN) method will be carried out, which is a classification method for a collection of data based on the majority of categories and the goal is to classify new objects based on attributes and sample samples from training data. So that the desired output target is close to the accuracy in conducting learning testing. The results of the test of the K-Nearest Neighbor method. It can be seen that from the K values ??of 1 to 10, the percentage of the results of the analysis of the K-NN method is higher than the results of the analysis of the K-NN method. And from the K value that has been tested, the K 2 value and the K 9 value have the largest percentage so that the accuracy is also more precise. As for the results of testing the K-Nearest Neighbor method in data classification. As for the author's test using a variation of 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%.</dc:description>
	<dc:publisher xml:lang="en">Yayasan Riset dan Pengembangan Intelektual (YRPI)</dc:publisher>
	<dc:date>2022-06-24</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.yrpipku.com/index.php/jaets/article/view/763</dc:identifier>
	<dc:identifier>10.37385/jaets.v3i2.763</dc:identifier>
	<dc:source xml:lang="en">Journal of Applied Engineering and Technological Science (JAETS); Vol. 3 No. 2 (2022): Journal of Applied Engineering and Technological Science (JAETS); 126-132</dc:source>
	<dc:source>2715-6079</dc:source>
	<dc:source>2715-6087</dc:source>
	<dc:source>10.37385/jaets.v3i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.yrpipku.com/index.php/jaets/article/view/763/476</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Journal of Applied Engineering and Technological Science (JAETS)</dc:rights>
</oai_dc:dc>