Judul: Peningkatan kinerja pelabelan otomatis citra menggunakan Bayesian Classifier pada temu kembali citra
Abstrak: This research proposes Bayesian Classifier to improve image annotation performance on image retrieval. Before being analysed by automatic annotation, descriptions of the image have to be known. Image description is the process of generating descriptions that represent the visual content of images in a certain manner, normally in the form of one or more features. Images are segmented into regions with grid segmentation. Each region are represented by a pre-specifed feature vector. The regions then clustered into a finite set of blobs. The correspondences between the blobs and the words are learned using Statistical Machine Translation and Bayesian Classifier. The experiment result shows that Statistical Machine Translation using Bayesian Classifier can improve precision as compared to Statistical Machine Translation. This method is promising to improve image query result on image retrieval.
Keyword: Content-based image retrieval, Automatic annotation, Statistical machine translation, Bayesian classifier, Latent semantic indexing, Bogor Agricultural University, Institut Pertanian Bogor, IPB

Judul: Improvement of Automatic Image Annotation Performance Using Bayesian Classifier on Image Retrieval
Abstrak: This research proposes Bayesian Classifier to improve image annotation performance on image retrieval. Before being analysed by automatic annotation, descriptions of the image have to be known. Image description is the process of generating descriptions that represent the visual content of images in a certain manner, normally in the form of one or more features. Images are segmented into regions with grid segmentation. Each region are represented by a pre-specifed feature vector. The regions then clustered into a finite set of blobs. The correspondences between the blobs and the words are learned using Statistical Machine Translation and Bayesian Classifier. The experiment result shows that Statistical Machine Translation using Bayesian Classifier can improve precision as compared to Statistical Machine Translation. This method is promising to improve image query result on image retrieval.
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Judul: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
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