Open AccessKlasifikasi Daun Dengan Perbaikan Fitur Citra Menggunakan Metode K-Nearest Neighbor
Author(s) -
Febri Liantoni
Publication year - 2016
Publication title -
ultimatics : jurnal ilmu teknik informatika/ultimatics : jurnal teknik informatika
Language(s) - English
Resource type - Journals
eISSN - 2581-186X
pISSN - 2085-4552
DOI - 10.31937/ti.v7i2.356
Subject(s) - pattern recognition (psychology) , artificial intelligence , k nearest neighbors algorithm , feature extraction , invariant (physics) , computer science , mathematics , mathematical physics
Plants are the most important part in life on earth as oxygen supplier to breathe, groceries, fuel, medicine and more. Plants can be classified based on its leaves shape. Classification process is required well data extraction feature, so it needs fixing feature process at pre-processing level. Combining median filter and image erosion is used for fixing feature process. Whereas for feature extraction is used invariant moment method. In this research, it is used leaves classification based on leaves edge shape. K-Nearest Neighbor Method (KNN) is used for leaves classification process. KNN method is chosen because this method is known rapid in training data, effective for large training data, simple and easy to learn. Testing the result of leaves classification from image which is on dataset has been built to get accuracy value about 86,67%.
Index Terms—Classification, Median Filter, Invariant Moment, K-Nearest Neighbor.