Texture Feature Extraction in Grape Image Classification Using
K-Nearest Neighbor

Texture Feature Extraction in Grape Image Classification Using
K-Nearest Neighbor

KNN, GLCM, grape, classification

Indonesian Grapes are a vine. This fruit is often found in markets, shops, roadside. Along with the development of computer
technology today, computers can solve problems by classifying objects and objects. How to apply GLCM and K-NN methods
for classification of grapes. The purpose of this study is to apply the GLCM and K-NN methods in the classification of grapes.
The dataset used from kaggle.com sources, the data tested are 3 types of grapes, the number of images is 2624. The fruit that
will be used for data collection and classification process is limited to three types of grapes, namely grape blue, grape pink
and grape white. How to apply GLCM and K-NN methods for classification of grapes. The feature extraction of GLCM used
in this study is the feature contrast, energy, correlation, and homogeneity. From testing the test data, the highest accuracy
value is 99.5441% with k = 2 at level 8, while the lowest accuracy value is 24.924% at each k level 2. The GLCM level value
is very influential on the accuracy results, namely the higher the GLCM level value, the higher the GLCM value. accuracy is
getting better

Pulung Nurtantio Andono1
, Siti Hadiati Nugraini2

Universitas Dian Nuswantoro Semarang

31-10-2022

Fajar bagus W

PDF

Indonesia

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