Verifikasi Citra Tanda Tangan Berbasis Perceptron

A signature has become an important human attribute, which can represent personal information. Human signatures are widely used to authorize documents, both paper-based as well as electronic-based ones. However, such authorization still poses various privacy issues, such as signature duplication and forgeries. These may not be easy to be addressed, particularly when involving many documents. Hence, advanced procedures are required to verify the signature authenticity. In this paper, we propose a new method for automatic signature verification based on the digitalized signature images. The method comprises successive image processing techniques, such as cropping, resizing, gray-scaling and thresholding. The binary images as the results of thresholding serve as the features of the signatures and are used to train a single layer Perceptron neural network. The experiment in this paper uses 42 digitalized signatures images, collected from two subjects. The obtained images are divided into the training and testing sets, in which the training and testing sets comprise 14 and 28 images, respectively. In the experiment, the proposed method produces the average training and testing accuracies of 100% and 98.85%, respectively. These indicate that the proposed method is reliable for practical applications

Penentuan Kombinasi Kernel Terbaik Menggunakan Median Filter

Many factors such as the use of low-quality lens, inappropriate image acquisition technique, and the uncertainty of environment condition affect digital image acquisition process. As a result, noisy image may be obtainedwhich is difficult to be recognized, processed, and analysed. The most occurred noise is salt and pepper which appears as white and black spots in image. This noise is usually removed or suppressed using median filter. However, improper size of median filter kernel may yield in poor result. This work aims to find the most suitable median filter kernel size combination using Breadth search, Depth Searchand Generate and Testmethods in order to obtain the best median filtering result. In this work, four median filter kernel sizes are used, i.e. 3×3, 5×5, 7×7, and 9×9. Filtering performance using each obtained combination is defined by measuring the Mean Square Error (MSE) of the filtered and unnoisy image (ground truth). Result shows that the best median filter kernel size combination is {3×3}. This combination is obtained using Breadth Search Method with the MSE of 27.0308

Pengaruh Tegangan dan Arus di Pengambilan Data Waktu Cahaya Matahari pada Perancangan Kontrol Intensitas Lampu Jalan Otomatis Tenaga Surya

INTISARIPada naskah ini akan dilakukan analisis nilai tegangan dan arus pada Alat Kontrol Intensitas Lampu Jalan Otomatis Tenaga Surya. Data tersebut diperoleh dari hasil pengujian alat terssebut dengan waktu/jam yang berbeda-beda untuk mengetahui efektivitas cahaya matahari dalam mengisi daya solar panel. Alat tersebut dibuat agar hemat energi dan sebagai energi alternatif. Sumber utama dari penerangan ini adalah dari energi matahari yang dikonversi oleh panel surya menjadi listrik, dan baterai atau aki berfungsi sebagai penyimpan energi listrik yang akan menyalakan lampu pada malam hari. Oleh sebab itu, apabila listrik PLN mengalami gangguan atau mati listrik maka penerangan jalan ini tidak terpengaruh.Dari alat di atas, diperoleh hasil pengujian untuk variable tegangan arus. Selanjutnya akan dianalisis pengaruh waktu/jam terhadap pengambilan data nilai tegangan dan arus. Pada data waktu pengisian panel surya, rata-rata tegangan dari solar cell sama yaitu 19,54 volt dan median (nilai tengah) sebesar 20 volt serta nilai rerata arus sebesar 0,81 A dan median sebesar 0,8 A. Semakin siang maka arus yang masuk ke baterai semakin besar karena energi dari sinar matahari berada pada puncaknya yaitu pada jam 11:00 sampai 13:00, kemudian semakin sore maka arus yang masuk semakin kecil karena sinar matahari yang diterima solar cell sudah tidak optimal.Kata kunci— solar panel, tegangan, arus, energi matahar