Identification of Flower Type Images Using KNN Algorithm with HSV Color Extraction and GLCM Texture

Due to the variety of types of flowers that exist and having and tracking each variety, making plant lovers and cultivators difficult to distinguish in determining the type of flower, it takes a very long time to find out the type of flower if you only rely on the five senses. With the application of the K-Nearest Neighbor algorithm and feature extraction of color and texture, it is very helpful in image processing to identify flowers more easily and shorten the time, with the greatest accuracy of 71% using the K-7 value, the flower was successfully carried out.

Authors

Publication Details

Journal
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Published
2025-04-16
DOI
https://doi.org/10.35870/jtik.v9i3.3826
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

Identification of Flower Type Images Using KNN Algorithm with HSV Color Extraction and GLCM Texture

Edhy Poerwandono, Muhammad Taufik
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Smart Agriculture and AI
article

Identification of Flower Type Images Using KNN Algorithm with HSV Color Extraction and GLCM Texture

Edhy Poerwandono, Muhammad Taufik
article en

Abstract

Due to the variety of types of flowers that exist and having and tracking each variety, making plant lovers and cultivators difficult to distinguish in determining the type of flower, it takes a very long time to find out the type of flower if you only rely on the five senses. With the application of the K-Nearest Neighbor algorithm and feature extraction of color and texture, it is very helpful in image processing to identify flowers more easily and shorten the time, with the greatest accuracy of 71% using the K-7 value, the flower was successfully carried out.

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)Vol. 9(3)
Openalex Percentile: Top 95%
Smart Agriculture and AI
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