Judul: Development of non destructive grading for export mangosteen using neural network
Abstrak: Grading of mangosteen for export in Indonesia, so far, is done manually based on visual evaluation. The practices cause ununi form quality, since theinside defects of fruits are undetected. Human errors also occur frequently due to the fatigue of the workers. This research aimed to develop a non destructive grading system to evaluate mangosteen quality for export using neural network. Input data for neural network were parameters obtained from image pr ocessing and ultrasonic measurement which had strong relationships with quality characteristics of mangosteen. The results indicated that area, red color index (r), blue color index (b), hue, saturation, intensity, and the velocity of ultrasonic wave were correlated with the physico-chemical characteristics of mangosteen. The accuracy level in evaluation of calyx wholeness using image processing was 100 %.
Keyword: Non destructive grading, Mangosteen, Neural network, Ultrasonic wavelength, Image processing, Pengolahan citra, Jaringan syaraf tiruan

Judul: Development of Mangosteen Maturity Classification Model on Color Based Using Fuzzy Neural Network.
Abstrak: Fuzzy Neural Network (FNN) has a capability to classify a pattern located within two different classes where a classical Neural Network (NN) is failed to do so. The fuzzy pattern classification is using membership degree on output of neuron as learning target. Objective of this research is to develop an artificial intelligence system model for non-destructive classification of fresh mangosteen using Fuzzy Neural Network. Component of color result in from image processing that influential against level of mangosteen’s maturity is used as input parameter. Percentage accuracy ratio of FNN model compare to NN for five, three, and two classification classes is 70:40, 86:65 and 90:90 respectively. The best result of FNN modeling is achieved on three class target classification (unripe, export and local) with green color index, value, a* u*, v*, entropy, contrast, energy and homogeinity as predictor parameters and 15 neurons hidden layer. Comparison of percentage capability of FNN against NN to identify the class is 100:0, 100:87 and 63:75.
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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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