Judul: Identifikasi tanaman jati menggunakan Probabilistic Neural Network dengan ekstraksi fitur ciri morfologi daun
Abstrak: Teak plant (Tectona grandis Linn. F) is one type of forest products that has considerable potential to be developed in Indonesia. The large number of teak plant species causes difficulties in identifying teak plant species identity. It takes knowledge of an expert in the field of teak plants to be able to identify the species of teak. The leaves are a source of easy observation and is available as a source of observations over time. This research proposed a new system to identify the teak plant leaves using Probabilistic Neural Network (PNN) classification and the extraction of characteristic features using Morphology. The system identifies 6 species of teak plant. The data are divided into four subsets and used as training data and test data. The PNN is trained using 90 leaves of the 6 species of teak plant. The identification using PNN produced 77.5% average accuracy.
Keyword: 

Judul: Identifikasi Daun Tanaman Jati Menggunakan Jaringan Saraf Tiruan Backpropagation dengan Ekstraksi Fitur Ciri Morfologi Daun
Abstrak: Teak identification is very useful in life. Knowledge about kind of teak is very important for teak farmers to understand the characteristics, benefits, as well as the buying and selling price of teak. This research built an automatic teak identification system using backpropagation Neural Network with leaf morphology feature extraction. The five basic leaf characteristics that were used in this research are area, perimeter, diameter, length, and width of the leaf. The five basic characteristics were then derived into 12 derivative characteristics. The data were the images of teak leaves of Biotrop, Emas, Jobika, Muna, Prima, and Super. Each data consists of 20 images with the size of 1200 x 2300 pixel. In order to obtain the best accuracy, k-fold cross validation with k=5 was used. The result shows that the best accuracy was 84.17% with 17 hidden neuron of neural network
Keyword: Artificial Neural Network (ANN), Backpropagation, Feature Extraction, Leaf, Teak

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.
Keyword: