Implementasi Transformasi Haar Wavelet untuk Deteksi Citra Jeruk Nipis yang Busuk
Abstract: Lime is a fruit that
has been widely cultivated and used in Indonesia. Many products use this fruit
in the production process. The process of sorting fruit is undeniably a very
substantial early process. It is necessary for large-scale be aware of this in term of result and time
required for the sorting process. Pattern Recognition is a discipline that
focuses on classifying or picturing an object based on characteristics or main
attribute of the object. In this research, the author implements Haar Wavelet
Transformation method by characteristic extraction based on colour and texture
, performs classification using
K-Nearest Neighbor (k-NN) to detect indication of rotten lime and the grade of
k on k-NN so the accuracy of the result could be acquired. Based on analysis
result, Haar Wavelet Transformation method is able to be implemented to detect
the indication of rotten lime and most optimal accuracy level of this system
reaches the number of 85 percent.
Penulis: Lidya Andriani
Sunjoyo, R. Gunawan Santosa, Kristian Adi Nugraha
Kode Jurnal: jptinformatikadd160996
