Kombinasi Algoritma KNN, HSV dan LBP Pada Pengolahan Citra Digital untuk Membedakan Kematangan Pisang
Keywords:
Android, HSV, K-NN, LBP, Banana RajaAbstract
The use of carbide in ripening bananas can result in chemical contamination of bananas. This can have a negative impact on the health of consumers who consume these bananas, so this research aims to design a system to differentiate between naturally ripe bananas and carbonated ripe bananas with Digital Images using the Hue Saturation Value (HSV), Local Binary Patterns (LBP) and K-Nearest Neighbor (K-NN). In this research, the system development used is UML (Unified Modeling Language). Meanwhile, making software in this system uses PHP, HTML, CSS, Java script software and for the database uses MySql. This research collects data obtained through observation, interviews and literature study. The method used to create this system is Hue Saturation Value (HSV) to extract color features, Local Binary Patterns (LBP) to extract texture features and the K-Nearest Neighbor (K) algorithm. -NN) for classification of plantain types. The digital image classification system distinguishes naturally ripe or carbitant plantains and can display classification results well so that it can help the public in distinguishing naturally ripened bananas or carbitants. The system created has been able to implement the Hue Saturation Value (HSV), Local Binary Patterns (LBP), and K-Nearest Neighbor (KNN) methods well and the system can differentiate naturally ripe bananas and carbonates well with a level of accuracy for the k= value 3, namely 100%, k=5 96.67%, k=7 93.33% and k=9 with an accuracy of 96.67% from 30 testing data using 200 training data.
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