Pears Classification Using Principal Component Analysis and K-Nearest Neighbor

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Moh. Arie Hasan Arief Setya Budi
Corresponding Author:
Moh. Arie Hasan | hasan.arie@gmail.com

Copyright (C):
Moh. Arie Hasan, Arief Setya Budi

Abstract

Pears is a fruit that is widely available in tropical climates such as in western Europe, Asia, Africa and one of them is Indonesia. There are many types of pears in Indonesia. Types of pears can be distinguished from the color, size, and shape. But it is still difficult for ordinary people to get to know the types of pears. This is what gave rise to the idea to conduct research related to image processing to classify three types of pears namely abate, red and william pears in order to help determine the type of pears. The pear type classification process is done by verify the image of pears based on existing training data. The research method used consisted of preprocessing image segmentation with morphological operations and feature extraction into Principal Component Analysis (PCA). The classification algorithm used is K-Nearest Neighbor (KNN). The use of adequate training data will further improve the classification of types of pears. The final results of this study amounted to 87.5%.

Keyword: Pears, Principal Component Analysis, Image Processing, K-Nearest Neighbor

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HASAN, Moh. Arie; BUDI, Arief Setya. Pears Classification Using Principal Component Analysis and K-Nearest Neighbor. SinkrOn, [S.l.], v. 4, n. 2, p. 34-41, mar. 2020. ISSN 2541-2019. Available at: <http://jurnal.polgan.ac.id/index.php/sinkron/article/view/10502>. Date accessed: 09 aug. 2020. doi: https://doi.org/10.33395/sinkron.v4i2.10502.
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