Judul: Genetic programming for medicinal plant family identification system
Abstrak: Indonesia is rich of biodiversity, but this richness is not fully utilized by the public. Due to lack of knowledge and information about medicinal plant, there are only 4.4 percent of the natural resources medicinal plants that been utilized. Difficulties in obtaining information about medicinal plants description caused people could not recognize the family or species of the medicinal plant in their environment. Generally, that information exists as text document form which is hard to be accessed. This study tries utilized critical information inside the document to identify the families of plant using heuristical approach, genetic programming. The botanical characteristic inside a document can be used to identify medicinal plant’s family, as every species has different characteristic and attributes. The Genetic Programming (GP) is able to determine the charasteristic or special feature of each family that is structurized into a tree form. The application of Genetic Programming in medicinal plant’s family identification system was aimed to assist the people in identifying medicinal plant’s family based on special appearance of each plant species on their environment. This research method consist of several phase of process, i.e. data acquisition, data booleanize, data partition into training and testing data, evaluation, and analysis. The booleanize process stated by binary numbers 1 and 0. The data as the result of booleanize process was divided into two, which are the training and testing data using 5-fold cross validation method. The training process used Genetic Programming to get the best individual, started by a generate rule or creating a number of individuals, and then those individuals was evaluated by an evaluation process called fitness evaluation. After that, genetical operation process was implemented, started by turnament process, which had an objective to select individual based on fitness value. The smaller fitness value gets the higher chances to be chosen on each generation. The crossover operation of recombining process at two individuals and development mutation operation from part of the individual was aimed to fleshing out the individual. The best individual is merely the expected solution, in the form of classification rule in identifying medicinal plants. The best classification rule with an average accuracy of 86.47% was obtained by population parameter of 10 000, and nodes of 24. Nodes consisted of function set (AND, OR, NOR) and terminal set (attribute/ special mark). In addition, the best classification rules also obtained by setting the crossover probability of 0.9, mutation probability of 0.1 and generation set to 10. The process of the training produced three rules in form of tree, which showed feature structure that differentiate each family. Those could be useful to identify the medicinal plants by public.
Keyword: booleanize, Genetic Programming, medicinal plants

Judul: Identifikasi Tumbuhan Obat Menggunakan Fitur Citra Morfologi, Bentuk, dan Tekstur dengan Klasifikasi Probabilistic Neural Network
Abstrak: Indonesia has no less than 2039 species of medicinal plants, but only 20-22% was cultivated, while around 78% obtained through direct collection (exploration) of forest. Generally, identification process of medicinal plants has been done manually by the herbarium taxonomist using guidebook of taxonomy/dendrology. It will be a difficult problem to identify directly it in the forest. Leave is usually used for identification because of its effectiveness and efficiency. This research proposed a new system to identify the medicinal plant leaves use Probabilistic Neural Network classification and to determine the character and specific classes of leaves regarding the best features. The method of identification is basic and derivative feature of leaf, Local Binary Patterns Variance, and Fourier Descriptors. After that, the features are classified by probabilistic neural network classifier which is combined Product Decision Rule (PDR). Then, the result will be analyzed based on overall effectiveness in class and image characteristics. The data of experiments consist of thirty species of flora at Biofarmaka Cikabayan and at Greenhouse Center of Ex-situ Conservation of Medicinal Indonesian Tropical Forest Plants, Faculty of Forestry, Bogor Agriculture University. The results without classifier showed that combination might only have a maximum until 63% accuracy, and if the three classifiers were combined with PDR, the accuracy will increase until 74.67%. Shape is feature with higher probability than morphology and texture feature.
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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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