Judul: Pengenalan Iris Mata Menggunakan Probabilistic Neural Network dengan Ekstraksi Ciri Log-Gabor Filter
Abstrak: One of the objects of research that is currently prevalent in the developed world is the individual identification technology based on biometrics. Iris biometric is promising for identification, because it has a consistent pattern compared with other types of biometrics. This study established a system of identification using iris images from CASIA eye using 1D log- Gabor filter for feature extraction and PNN as classifier. This system uses automatic segmentation based on the threshold to localize the iris collarette region and normalize the results to the constant dimension using Daugman’s dimensional rubber sheet model by mapping each point in the iris region to a pair of polar coordinates. Data were divided into three subsets. Tests were conducted on the left eye dataset, right eye dataset and a combined dataset of left eye and right eye. The average accuracy produced for the left eye dataset is 98%, for the right eye dataset is 97%, and for the combination of left and right eye is 100%.
Keyword: probabilistic neural network (PNN)., log-Gabor filter, iris recognition

Judul: Pengenalan Iris Mata dengan Support Vector Machine Menggunakan Ekstraksi Ciri Log-Gabor Filter
Abstrak: The human eye is one of the identifying characteristics of an individual, especially the iris part. Human iris has a consistent pattern and more reliable than the other biometric subjects such as face and fingerprint which change over time. This research uses the CASIA iris image data. The system uses an automatic segmentation based on threshold to localize the iris collarette and normalize the results to constant dimension using Daugmans rubber sheet model by remapping each point within the iris region to a pair of polar coordinates. The features are extracted using the 1D log-Gabor filter produce a template which will be used as features for the classification process. The template data will be classified by using One-against-all Support Vector Machine (SVM) method. The data are split into five subsets and then alternately used for training data and testing data. These method used three kinds of kernel: Linear kernel, Polynomial kernel, and Radial Basis Function (RBF) kernel. The tests are performed on a dataset of the left eye, a dataset of the right eye, and the combination of both eyes. The best iris recognition accuracy of the dataset for the right eye is at 99%, for the left eye is at 100%, and for the combination of both eyes is at 100%. According to these results, it can be concluded that if there is only one iris image for each individual, then the SVM method is appropriate to be used for iris recognition. The training time required for the dataset of the left eye, the dataset of the right eye, and the combination of both eyes are 0.3368 seconds, 0.3366 seconds and 0.6857 seconds, respectively, while testing time required for a dataset of the left eye, a dataset of the right eye, and the combination of both eyes are 0.0361 seconds, 0.0364 seconds and 0.0638 seconds, respectively.
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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.
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