Bagi orang awam, mengetahui tumbuhan berdasarkan warna daun tentu tidak mudah, mengingat semua warna daun relatif sama yaitu warna hijau, sehingga akan sulit juga untuk mengetahui manfaat dari tumbuhan tersebut. Oleh karena itu dibutuhkan sebuah sistem klasifikasi berdasarkan warna daun untuk mengetahui apa nama dan manfaat tumbuhan tersebut. Penelitian ini bertujuan untuk menghasilkan ekstraksi nilai red, green, blue, hue, saturation, dan value pada citra daun, menghasilkan klasifikasi citra daun berdasarkan hasil ekstraksi nilai RGB dan HSV, serta menghasilkan nilai akurasi hasil klasifikasi citra daun. Pada penelitian ini, peneliti akan melakukan klasifikasi terhadap daun berdasarkan warna daun. Peneliti menggunakan 200 buah citra dari 10 jenis daun. Klasifikasi daun berdasarkan warna dilakukan peneliti menggunakan ruang warna RGB dan HSV. Hasil klasifikasi citra daun memiliki rata-rata akurasi yang tinggi yaitu 90,08%.
One important part of the eye that is critical for processing visual information before it is sent through the optic nerve to the visual cortex is the retina. The retina of each individual has its own uniqueness that can be used as a characteristic feature in identifying, verifying, and authenticating. The traditional authentication process has various weaknesses such as forgetting the PIN code or losing the ID card used for obtaining system authentication. The results of extracted retinal blood vessels can be used as a feature in the formation of an individual identification system. In the imaging using a fundus camera, the retina's blood vessel has distinguishing shape and number of candidates from one human retina to another. In this research, researchers will develop an algorithm for extracting the retinal fundus image's blood vessels. The feature extraction is done by taking the fundus image feature which is the blood vessel as one of the unique characteristics in forming an individual identification system. The number of blood vessel candidates will then be calculated from the extracted blood vessel result. This research uses wavelet function by looking at the very complex texture of blood vessels using the approximation coefficient. The direction detail coefficient on the wavelet is also used to perform the extraction of retinal blood vessels where the structure of the retinal blood vessels in the fundus image is in all directions. The results of these blood vessel candidates will be used in further research to formulate a biometric system that is formed by unique features in the retinal fundus image which will be used to identify individuals using body traits.
Authentication is needed to enhance and protect the system from vulnerabilities or weaknesses of the system. There are still many weaknesses in the use of traditional authentication methods such as PINs or passwords, such as being hacked. New methods such as system biometrics are used to deal with this problem. Biometric characteristics using retinal identification are unique and difficult to manipulate compared to other biometric characteristics such as iris or fingerprints because they are located behind the human eye thus they are difficult to reach by normal human vision. This study uses the characteristics of the retinal fundus image blood vessels that have been segmented for its features. The dataset used is sourced from the DRIVE dataset. The preprocessing stage is used to extract its features to produce an image of retinal blood vessel segmentation. The image resulting from the segmentation is carried out with a two-dimensional image transformation such as the process of rotation, enlargement, shifting, cutting, and reversing to increase the quantity of the sample of the retinal blood vessel segmentation image. The results of the image transformation resulted in 189 images divided with the details of the ratio of 80 % or 151 images as training data and 20 % or 38 images as validation data. The process of forming this research model uses the Convolutional Neural Network method. The model built during the training consists of 10 iterations and produces a model accuracy value of 98 %. The results of the model's accuracy value are used for the process of identifying individual retinas in the retinal biometric system.
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