Prototype of Melon Fruit Quality Sorter Based on Skin Texture Using Local Binary Pattern Histogram
DOI:
https://doi.org/10.33019/jurnalecotipe.v10i2.4476Keywords:
KNN, Latte Panda, Local Binary Pattern, Melon, Quality Sorter, WebcamAbstract
Cucumis Melo L is one of the popular fruits in Indonesia. The numerous benefits and various contents of melon make it highly valuable for human health. Determining the quality of melon is crucial, considering its numerous benefits and contents. The quality of melon is determined by the presence of net-like patterns on the surface of the fruit's skin when it is ripe for harvest. Currently, conventional methods that rely on direct visual observation are still commonly used to sort the quality of melons. Therefore, a prototype system was developed to sort the quality of melons based on the texture of their skin. The purpose of this system is to reduce errors caused by eye fatigue and variations in accuracy during the sorting process. In this research, the feature extraction method of the local binary pattern (LBP) was employed, along with the K-Nearest Neighbor (KNN) method for classification. The classification was divided into two grades: Grade A and Grade B melons. The testing phase involved 200 data samples, with each grade consisting of 100 data samples. The results of the testing phase showed a success rate of 96% for the system. Based on the percentage, it can be concluded that the system has successfully performed well in sorting the quality of melons based on the texture of their skin.
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