Judul: Pengenalan Sidik Jari Menggunakan Resilient Backpropagation Neural  Network dengan Praproses Transformasi Wavelet
Abstrak: Sistem pengenalan biometrik menggunakan karakteristik fisiologis yang dimiliki manusia sebagai dasar dari pengenalannya. Karakteristik fisiologis yang digunakan harus bersifat unik, permanen, dan universal. Sidik jari memiliki semua karakteristik tersebut sehingga digunakan sebagai dasar pengenalan dalam penelitian ini. Data yang digunakan pada penelitian ini berjumlah 70 buah sidik jari yang diperoleh dari situs internet pada alamat http://www.bias.csr.unibo.it/fvc2000/databases.asp. Sidik jari tersebut diambil dari 10 orang berbeda dengan 7 posisi pengambilan yang serupa. Data akan mengalami transformasi wavelet sebanyak enam level dengan menerapkan bank filter wavelet. Induk wavelet yang digunakan pada penelitian ini adalah Haar. Citra yang akan digunakan pada proses pengenalan sidik jari adalah citra pendekatan hasil dekomposisi wavelet level 1, level 2, level 3, level 4, level 5, dan level 6.
Keyword: 

Judul: Pengenalan iris mata dengan Backpropagation Neural Network menggunakan praproses transformasi wavelet
Abstrak: Biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Data used for this research are obtained from CASIA dataset consisting of 600 eye images from 30 people with 10 left eye images and 10 right eye images from each person. The data are divided into two categories, training data (360 images) and testing data (240 images). Each image contains eyelid, eyelash, sclera, iris, and pupil. The system uses an automatic segmentation based on a threshold to localize the iris collarette. A rubber sheet model is then used to normalize the image into a 20×240 matrix. Feature extraction is conducted using Haar wavelet transformation at level 0 (original image), level 1, level 2, level 3, and level 4. The features are fed into a backpropagation neural network for classification. Parameters observed in this research are number of hidden neurons, learning rate, and momentum at each level wavelet decomposition. Maximum generalization and training time are used as the performance metrics. The best parameters obtained at each level wavelet decomposition are used in testing using 5-fold cross validation. The best recognition of testing data considering the maximum generalization and the training time was obtained from wavelet decomposition level 3.
Keyword: 

Judul: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
Keyword: