Department of Informatics, Universitas Dian Nuswantoro, Indonesia
BibTex Citation Data :
@article{JTSISKOM12990, author = {Fittria Shofrotun Ni'mah and T Sutojo and De Rosal Ignatius Moses Setiadi}, title = {Identifikasi Tumbuhan Obat Herbal Berdasarkan Citra Daun Menggunakan Algoritma Gray Level Co-occurence Matrix dan K-Nearest Neighbor}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {6}, number = {2}, year = {2018}, keywords = {digital leaf image identification; herbal medicinal plants; GLCM analysis; KNN classification}, abstract = { Medicinal plants can be used as an alternative natural treatment, instead of chemical drugs. But because of too many types of plants and lack of knowledge, it will be difficult to identify these herbs. Computer assistance can be used to facilitate the identification of these herbs. This research proposes the identification of herbal plants based on leaf image using texture analysis. There are 10 types of herbal medicinal plants used in this study. The texture analysis used was GLCM by extracting contrast, correlation, energy, and homogeneity. Classification is done by KNN. The result of the experiment showed that the accuracy of identification using 9-fold cross-cross validation method was 83.33% using 9 subsets. }, issn = {2338-0403}, pages = {51--56} doi = {10.14710/jtsiskom.6.2.2018.51-56}, url = {https://jtsiskom.undip.ac.id/article/view/12990} }
Refworks Citation Data :
Medicinal plants can be used as an alternative natural treatment, instead of chemical drugs. But because of too many types of plants and lack of knowledge, it will be difficult to identify these herbs. Computer assistance can be used to facilitate the identification of these herbs. This research proposes the identification of herbal plants based on leaf image using texture analysis. There are 10 types of herbal medicinal plants used in this study. The texture analysis used was GLCM by extracting contrast, correlation, energy, and homogeneity. Classification is done by KNN. The result of the experiment showed that the accuracy of identification using 9-fold cross-cross validation method was 83.33% using 9 subsets.
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