Judul: Modeling and Optimization of Support Vector Machine (SVM) for Detection of Human Eye and 2DPCA as Feature Extraction
Abstrak: Eye tracking has become one of the most important processes in the human computer interaction. Eye tracking has also been shown to be useful in various applications. Specific applications included in this system are the system in handwriting reading, music reading, human activity recognition, perceptions of advertising , sport playing, Human Computer Interaction (HCI), especially for people with disabilities, medical research and other areas. Eye detection is the most important thing to do as an initial basis in the application of eye tracking. By getting the position of the eye, eye tracking will be easier to do at a later stage (Bhoi and Mihir 2010) . In general, eye detection is done in two steps. It is determine the location of the face to extract the eye area then eye detection from the eye area. The purpose of this research is perform the modeling and optimization of kernel parameters of SVM with GA for human eye detection with 2DPCA as feature extraction. The research methodology consists of several stage. These are data collection, data preprocessing, feature extraction, modeling the human eye detection with SVM and GA, manufacture training and testing system for eye detection, analysis, and research report writing. Experiment conducted with feature extraction using the 2DPCA. The results obtained then validated using the Euclidean distance. The experiment results gained 68.97% accuracy on the dimension (d) = 2. The next experiment was performed using the SVM algorithm for the classification algorithm. Kernel used in SVM is linear, RBF, and polynomial. The results of the three kernel maximum accuracy is obtained with the RBF kernel parameters C = 3 and log2 Gamma = -1. This research is made using Matlab R2008b version 7.7.0.471. Eye detection model feature extraction using SVM with 2DPCA produce the highest accuracy on the RBF kernel with value Log2 Gamma = -1 and C > 0 with a value of 99.97 % on the training data and 88.16 % on the testing data.
Keyword: 2DPCA, eye detection, eye tracking, GA, SVM

Judul: Development of Face Recognition Model using Bi-2DPCA and Support Vector Machine
Abstrak: The human face is always used in the application of access control systems, identity authentication systems, surveillance systems and security systems. Some researh on face recognition has been proposed with a variety of methods, such as Eigen Faces, Fisher’s Linear Discriminant, Bayesian Networks, Neural Networks,Hidden Markov Model, and so on. But all the above mentioned methods have limitations, such as dealing with the size of the image and lighting and learning time are too long. In a study to obtain an alternative method for face recognition, Le TH and Bui L (2011) conducted research model of face recognition with Two- DimensionalPrincipal Component Analysis (2DPCA) and Support Vector Machine (SVM) using AT&T face image data. The results of these studies that the accuracy of face recognition use 2DPCA and SVM was 97.3% and PCA-SVM was 95.2%. Then in 2010, Bi-2DPCA uses as alternative method of face recognition performed by Yang J et al. (2010). In a study comparing methods of Bi-2DPCA, 2DPCA and PCA. The conclusion that the use of Bi-2DPCA generating and computational accuracy was better than 2DPCA and PCA Based on research conducted by Le TH and Bui L (2011) and Yang J et al. (2010), conducted research for the development of models of face recognition using dimension reduction technique Bi-2DPCA and Support Vector Machine (SVM) as pattern recognition. 1DPCA and 2DPCA dimension reduction technique are used as comparison measure dimensions and computing time than using Bi-2DPCA technique. To help SVM in recognizing human faces, SVM use linear, polynomial and radial basis function (RBF) kernel function. When using RBF kernel, used for the RBF kernel, the parameter for sigma is 2-15 up to 23 and the value of C starting from 2-5 up to 214 which have been used in a study conducted by Le TH and Bui L (2011). Facial image data used are taken from the ORL Face Database AT&T Laboratory, which contain 40 faces, each face with 10 images, and total of image 400 pieces. Validation is done by Leave-One-Out (LOO) cross validation, so that for each pair of extraction and classifier method is 10 trials. One against all method used to help SVM in classifying the ORL face data consists of 40 classes, to reduce the computational time. From the results of tests on 36 models of face recognition , Bi-2DPCA85% with the help of a linear kernel generates the highest level of face recognition accuracy of 94.25 %, and the lowest accuracy rate of 24.50 % was obtained with 1DPCA-kernel polynomial ordo 3 . While the lowest computational time of 15.34 seconds when component is used by 85 % computation time and a maximum of 252.68 seconds when component is 95 %. There are several things that could be concluded that: pattern accuracy on each kernel feature extraction are the same. This means that there is no relationship between the feature extraction used by the kernel to the SVM. The second is, in this study SVM cannot classify large dimension data. From the results of tests on 36 models of face recognition, Bi2DPCA85% with the help
Keyword: Face Recognition, Principle Component Analysis, Kernel, Support Vector Machine, Leave-one Out Cross Validation

Judul: Pengusahaan tembakau vorstenlands di PNP XIX kebun Kebonarum Klaten
Abstrak: Tembakau merupakan salah satu komoditi yang mempunyaі arti ekonomi penting dan merupakan penghasil devisu sala. Walaupun bukan merupakan suatu bahan kebutuhan pokok, namun kebutuhan terhadap tembakau setiap tahunmya terus meningkat...
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