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Journal : Petir

Penggunaan Global Contrast Saliency dan Histogram of Oriented Gradient Sebagai Fitur untuk Klasifikasi Jenis Hewan Mamalia Yohannes Yohannes; Muhammad Ezar Al Rivan
PETIR Vol 13 No 1 (2020): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (655.511 KB) | DOI: 10.33322/petir.v13i1.908

Abstract

Mammal type can be classified based on the face. Every mammal’s face has a different shape. Histogram of Oriented Gradient (HOG) used to get shape feature from mammal’s face. Before this step, Global Contrast Saliency used to make images focused on an object. This process conducts to get better shape features. Then, classification using k-Nearest Neighbor (k-NN). Euclidean and cityblock distance with k=3,5,7 and 9 used in this study. The result shows cityblock distance with k=9 better than Euclidean distance for each k. Tiger is superior to others for all distances. Sheep is bad classified.
Klasifikasi Jenis Jamur Menggunakan SVM dengan Fitur HSV dan HOG Yohannes Yohannes; Daniel Udjulawa; Timoteus Ivan Sariyo
PETIR Vol 15 No 1 (2022): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/petir.v15i1.1101

Abstract

Mushrooms are one of the plants that have so many varieties. Every variety has a different shape and color. But most people still feel difficult to know and classify every mushroom. Therefore, classification for mushroom is needed. Method for this research are Hue Saturation Value (HSV) as color segmentation, then Histogram of Oriented Gradient (HOG) as feature extraction, and Support Vector Machine (SVM) as a classification method. Mushrooms that being use are Agaricus, Amanita, Boletus, Cortinarius, Entoloma, Hygrocybe, Lactarius, Russula, Suillus. Total of mushrooms for this research are 900, with 100 each genus. This research using the k-fold Cross Validation method for 4-fold. From 900 images there are 675 for the training phase and 225 for the testing phase. Overall for this research got precision, recall, accuracy respectively 23.80%, 22.94%, and 82.69%. The best mushroom was Boletus with precision, recall, accuracy respectively 55.37%, 46.84%, and 89.69%.