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: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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