Identification of Dayak Onion with Shape and Texture Feature Extraction by Image Processing

Joan Angelina Widians, Anindita Septiarini, Masna Wati, Andi Tejawati, Maratus Soleha, Tjikoa Ade Fiqri


Dayak Onion (Eleutherine Palmifolia (L.) Merr) is a medicinal plant that has expanded in East Kalimantan. Humans select the type of onion very quickly, but not for computers. Human perception tends to be subjective to an object because there are factors in the composition of color, shape, and texture owned by the entity thing. The study aims to identify Dayak onions with digital images and apply the K-Nearest Neighbor (KNN) method to extract shape and texture features. There are five types of onions: Dayak onions, Shallot, Bombai onions, Garlic, and Lanang onions. K-Nearest Neighbor (KNN) is a method for objects classification on data learning that is the closest distance or has the most characteristic similarities to an object. This method is for the Dayak Onion’s identification and classification. The accuracy level of the KNN method is on shape and texture feature extraction to identifying Dayak onions using 100 image data. Overall, the best accurate result with the KNN method is 86.66%.


Dayak Onion; Shape Feature; Texture Feature; Feature Extraction; K-Nearest Neighbor (KNN); Image Processing

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