Fuzy Yustika Manik, S. Kana Saputra, Dewi Sartika Br Ginting
Indonesia is one of the countries with high plant diversity. Almost every region in Indonesia has distinctive plants and may not be present in other countries. Based on these facts required a strategic step to record and identify plants in Indonesia. One method that can be used to leaf image feature extraction is the Gray Level Co-occurrence Matrix (GLCM). This research will implement k-Nearest Neighbor (k-NN) method to classify type of plants based on leaf texture. The classification result based on GLCM using k-NN classifier showed that the accuracy using k = 3 was 83%. The use of parameter k influence classification results, the greater the value of k then the accuracy would be smaller. Classification errors for some types of leaf images occurred because the value extraction traits generated by GLCM was very similar and had a small range of values. © Published under licence by IOP Publishing Ltd.
Program Studi Ilmu Komputer, Fakultas Ilmu Komputer Dan Teknologi Informasi, Universitas Sumatera Utara, Medan, Indonesia; Program Studi Ilmu Komputer, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Negeri Medan, Medan, Indonesia
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