Department of Computer Science, Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri. Jalan Kramat Raya No. 18, Senen, Jakarta Pusat, DKI Jakarta 10450, Indonesia
BibTex Citation Data :
@article{JTSISKOM14013, author = {Moh. Arie Hasan and Yan Riyanto and Dwiza Riana}, title = {Klasifikasi penyakit citra daun anggur menggunakan model CNN-VGG16}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {9}, number = {4}, year = {2021}, keywords = {k-means clustering; VGG16 transfer learning; grape leaves classification; CNN}, abstract = {This study aims to classify the disease image on grape leaves using image processing. The segmentation uses the k-means clustering algorithm, the feature extraction process uses the VGG16 transfer learning technique, and the classification uses CNN. The dataset is from Kaggle of 4000 grape leaf images for four classes: leaves with black measles, leaf spot, healthy leaf, and blight. Google images of 100 pieces were also used as test data outside the dataset. The accuracy of the CNN model training is 99.50 %. The classification yields an accuracy of 97.25 % using the test data, while using test image data outside the dataset obtains an accuracy of 95 %. The designed image processing method can be applied to identify and classify disease images on grape leaves.}, issn = {2338-0403}, pages = {218--223} doi = {10.14710/jtsiskom.2021.14013}, url = {https://jtsiskom.undip.ac.id/article/view/14013} }
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