Judul: Visualisasi Data Berkelompok dengan Analisis Komponen Utama Kernel
Abstrak: Visualization is engineered in making drawings, diagrams or animations to the appearance of an information. Visualization is often used in exploratory data analysis to summarize the main characteristic of the data. Its main goal is to communicate information clearly and effectively to users through graphs, plots, and tables so it makes the data to be more accessible, understood and used. For classified data, visualization can be done directly by plotting the data object in the ordinary space. But, for the data with dimension is greater than three, the visualization cannot be done directly. It can be done by using Principal Component Analysis (PCA). PCA is used to reduce the dimensionality of the data to be the new data with smaller dimensions, in this case into 2-dimensional space, so it can be visualized. The new variables that represent the data are called principal components. However, PCA could not model the high complexity data with nonlinear relationships among variables. Therefore, Kernel PCA was developed as the generalization of ordinary PCA. Implicitly, Kernel PCA is a nonlinear form of ordinary PCA which maps the original data that have nonlinear relationships among variables into highdimensional feature space. At the space, the data obtained has linear relationships among variables so that ordinary PCA can be applied. This study aimed to compare the result of visualization between PCA and Kernel PCA in separating the data. In Kernel PCA, kernel function used in this study is Gaussian kernel function with  as parameter. The ability of Gaussian kernel function to map the classified data into feature space to obtain the separation among classes is very dependent on the parameter. So that, before using Kernel PCA, we need to assess the selection method for parameter σ in Gaussian kernel function to visualize of classified data that minimize misclassification. The classification used Fisher linear discriminant analysis to show the separation of the data in the feature space. By empirical study of the two synthetic data sets, namely Wang Synthetic Data set and Synthetic Control Chart Time Series Data set and two real world data sets, namely Iris Data set and Wine Data set, we obtained that Kernel PCA gave better results in visualizing the separation of the data compared with PCA. Parameter values in Gauss kernel function for Kernel PCA which provides results visualization to minimize misclassification is proposed to be selected in the interval min ,max  ; . i j i j   
Keyword: Kernel PCA, Gaussian kernel function, visualization, Fisher discriminant linear analysis

Judul: Klasifikasi dengan Analisis Komponen Utama Kernel
Abstrak: Principal component analysis (PCA) is a special case of the kernel PCA with linear kernel function. The aim of this study is to resolve the data problem that is not linearly separated and to classify an object into a group by using kernel PCA to obtain the smallest classification error. Group classification using kernel PCA is performed by the linear and Gaussian kernel function. The result of the study shows for wine recognition data with the linear and Gaussian (parameter 2.5) kernel function produces 30.889% and 17.416% classification error, respectively
Keyword: classification error, kernel, principal component analysis

Judul: Amfibi sebagai satwa peliharaan: Ekspor, Impor dan Perdagangan Domestik
Abstrak: Indonesia has exported amphibian as frozen frog legs (listed since 1969 under fishery product) for consumption and live amphibian (listed since 1983 under reptilian class in forestry product) for pets. There are also live amphibian trading in local level i.e in markets and malls. This research aims to identify amphibian species exported and imported by investigating document from related administration and to identify amphibian species traded in domestic market by observation at Jakarta. Research is primarily conducted by processing quota data taken from Department of Forestry Republic of Indonesia database in Jakarta. Market observation was carried out in 18 locations (3 markets and 15 malls) in Jakarta. I carried out interview with trader and export business, which related to net trading live amphibians for pets. Respondent was choosen using snowball sampling manner. Name of species is written according to Iskandar & Coljin (2000). If there is no such record in the checklist (for example species are from outside Indonesia), then the species name is written according to Frost (2004). There are 56 species from eight families of wild amphibians that have pets quota between 1998-2008. Nineteen species was traded since 1983. The highest export quota is Litoria caerulea (21,37%). Allocation of highest catching quota is for BKSDA Jayapura (20,40%) and BKSDA Papua Barat (18,96%). Four species are included in IUCN Red List categories as vulnerable, among others are Limnonectes macrodon, Meristogenys jerboa, Nyctixalus margaritifer and Rhacophorus margaritifer. The number of amphibians that have export realization are 39 species. Two species of amphibian, Leptobrachium hasseltii and Rhacophorus margaritifer are amphibian species whose been known infected by chytrid fungus in the wild. The biggest species exported is Litoria caerulea (54,34%). Between 1983-1991, the biggest import amphibian for pets from Indonesia (66,13%) is Netherlands. Number of quota was relative stable in the last eight years, where as the number of export realization is more fluctuates. Amphibian species imported and re-exported between 1983-2008 come from Africa, South Asia, Malaysian, Archipelago Oceania, North America, Central America and South America. The number of pet amphibian species traded at domestic market is less than exported. Data showed that Indonesia imports 15 amphibian species between 1983-2008, However I found different species in the market that was unrecorded in the Ministry of Forestry data, which is the Fire Belly Salamander.
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