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.
Keywords: Pattern Recognition, k-NN, Haar Wavelet
Penulis: Lidya Andriani Sunjoyo, R. Gunawan Santosa, Kristian Adi Nugraha
Kode Jurnal: jptinformatikadd160996

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