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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Sandra Mareza Adelfi
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Computer Science & IT

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Klasifikasi Daun Herbal Berdasarkan Fitur Bentuk dan Tekstur Menggunakan KNN Meiriyama Meiriyama; Siska Devella; Sandra Mareza Adelfi
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 3 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i3.2974

Abstract

Indonesia has an abundance of biodiversity. From a total of 40,000 types of herbal plants known in the world, there are approximately 30,000 types of herbal plants in Indonesia. Herbal plants are plants that are commonly used by people, especially in Indonesia, which have biodiversity as ingredients for making herbal medicines. Herbal plants are certainly not easy to recognize even though they often grow around the environment. Because there is still a lack of community knowledge about herbal plants, it is not possible to use these herbal plants. This study aims to classify the leaves of herbal plants using the K-Nearest Neighbor (KNN) method with k value is 3 and feature extraction of Histogram of Oriented Gradient (HOG) and Local Binary Patterns (LBP). The research was conducted on 15 types of herbal plants. Accuracy HOG method with KNN is 92.67%, Accuracy LBP with KNN is 88.67% and accuracy combination of HOG and LBP features with KNN method is 92.67%. Based on the three experiment scenarios that have been carried out, it shows that the combination of HOG and LBP features does not affect the accuracy of leaf classification of herbal plants.
Co-Authors Devella, Siska Meiriyama, Meiriyama