Application of Gray Level Co-Occurrence Matrix Using the Self Organizing Map Method in Detecting Areca Fruit Ripeness
DOI:
https://doi.org/10.22303/csrid-.16.2.2024.174-187Keywords:
GLCM, Self Organizing Map, Buah Pinang, RGB, HSVAbstract
Areca nut can be seen through its fiber which plays an important role in improving digestion. Fiber helps facilitate bowel movements and prevent constipation, provides improvements in the digestive system and keeps teeth healthy. The results of this research obtained a classification model using the Gray Level Co-Occurrence Matrix. Many areca nut plantations still use manual methods to sort fruit, but this method is often inaccurate and varies, this is due to differences in the perceptions of each person. Histograms help you find images with similar color composition. Similarity is measured by calculating the distance between histograms. Color composition can be seen in the form of a histogram which represents the distribution of the number of intensity pixels for each color in an image. This research aims to detect the ripeness of areca nut fruit. This research uses a combination of RGB and HSV feature extraction techniques and GLCM extraction techniques. The resulting information is in the form of a percentage of similarity and classification of fruit maturity which includes Ripe (Hue=0.11893, saturation= 0.75727, value= 0.81813), half ripe (Hue= 0.17933, Saturation=0.20123, value= 0.44968) Unripe (Hue=0.21514, Saturation= 0.47934, Value= 0.36719) with an accuracy level of 100%, from images that have been processed.
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