• Firnanda Al-Islama Achyunda Putra Putra Universitas Merdeka Malang
  • Aditya Galih Sulaksono Sulaksono Universitas Merdeka Malang
  • Listanto Tri Utomo Utomo 3listanto.utomo@unmer.ac.id
  • Ahmad Rizal Khamdani Khamdani Universitas Merdeka Malang
Keywords: Fruit Classification, HOG, K-NN


Abstract - Fruits come from plant pistils and tend to have seeds, while vegetables can come from nuts, leaves, or grains that can be cooked. There is a great variety in shape, color, and texture of fruits and vegetables, but it is sometimes difficult to tell the difference between types that share these similarities. Therefore, a system is needed to help classify fruits and vegetables more easily. In this study, the types of fruits and vegetables were classified based on the extraction results from the Histogram Oriented of Gradient. The method used in this study is the Histogram Oriented of Gradient (HOG) and K-Nearest Neighbor (K-NN). The HOG process is used for feature extraction, namely to obtain the characteristics of fruits and vegetables, while the K-NN is used for the image classification process. Each training image and test image weight values ​​will be compared by minimizing the Euclidean value. Research with this method gives test results with an accuracy rate of 76.54% for fruits, while the test results for vegetables give an accuracy value of 71.22%.


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How to Cite
Putra, F. A.-I. A. P., Sulaksono , A. G. S., Utomo, L. T. U., & Khamdani , A. R. K. (2023). KLASIFIKASI BUAH DAN SAYUR MENGGUNAKAN FITUR EKSTRAKSI HOG DAN METODE KNN . Jurnal Informatika Polinema, 10(1), 45-52. https://doi.org/10.33795/jip.v10i1.1433