Open AccessK-Nearest Neighbor Algorithm to Identify Cucumber Maturity with Extraction of One-Order Statistical Features and Gray-Level Co-Occurrence
Author(s) -
Syamsudduha Syahrorini,
D. S. Nor Syamsudin,
Dwi Hadidjaja Rasjid Saputra,
Akhmad Ahfas
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/819/1/012010
Subject(s) - kurtosis , skewness , grayscale , mathematics , artificial intelligence , pattern recognition (psychology) , pearson product moment correlation coefficient , entropy (arrow of time) , k nearest neighbors algorithm , pixel , statistics , computer science , physics , quantum mechanics
Determination of the maturity of cucumber fruit after harvest is subjective. The level of thoroughness of each individual’s cucumber selection is different. The maturity of cucumbers is seen from the age of the fruit, the resemblance of ripe cucumbers or old, raw or young is difficult to distinguish in terms of the texture of the fruit skin. The use of red, green, blue, and grayscale color modes from the cucumber imagery, is then processed using the extraction of the statistical features of the Order-One and Order-Two GLCM methods. Imagery using a 13 megapixels smartphone camera. Texture parameter values are used in the first order: mean, variance, skewness, kurtosis, entropy. Second order: energy, contrast, correlation, inverse different moments, angular second moments, variance 2, and entropy 2. The classification of the texture parameters of both order uses an algorithm from K-Nearest Neighbors as a comparison of test data values and training data. So that the system is made can identify the maturity of cucumbers old and young. The highest accuracy is found in grayscale imagery with a combination of two skewness and kurtosis parameters and euclidean distance calculation in K-Nearest Neighbor classification of 96.05%.