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.
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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: Sintesis Zink Diisoamilditiofosfat pada Medium Heptana dan Karakterisasi Kinerja Inhibisinya terhadap Korosi Logam Tembaga yang Diukur secara Polarisasi Potensiodinamik
Abstrak: Zinc dialkyldithiophosphates (ZDTP) compound is known as a lubricant additive and has many functions, one of which is as anti-corrosion. ZDTPi was synthesized in 2 steps. The first step was formation of intermediates so called dialkyldithiophosphates acid (ADTP) as resulted from the reaction between diphosphorus pentasulfide (P2S5) and isoamyl alcohol. The second step was addition of metal oxide, ZnO. ZDTPi performance as an anti-corrosion was measured by potentiodynamic polarization technique. The inhibiton effectiveness of ZDTPi to metal Cu reached 98.77% in the 3% concentration of inhibitor solution. Thermodynamic parameter showed a decreasing spontaneity reaction in the presence of inhibitor. The activation energy increased due to the increasing inhibitor addition as indicated by the increasing minimum energy of reaction.
Keyword: Bogor Agricultural University (IPB), zink dialkyldithiophosphates, thermodynamic, potentiodynamic polarization, dialkyldithiophosphates acid, anti-corrosion