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: Klasifikasi Dokumen Tumbuhan Obat Berbasis Famili Menggunakan Probalistic Neural Network
Abstrak: This research presents a system for the extraction of features and classification of the document medicinal plants using the chi-square method and neural network classification probalistic. Automation family identification by grouping characteristics medicinal plants contained in the document. In this research, the phase consists of collecting documents, doing conversion from hardcopy documents to softcopy into XML format, pre-process the document, featuring selection using the chi-square, document classification using PNN and evaluation using Confusion Matrix. Classification results are influenced by the number of family identifier words. Number of words identifier of a family affected by the same number of words in each document training. The more same words in each document training, the greater probability of the word being said identifier. In this research, identifier of a family does not describe the characteristics of plant taxonomy. This is due to the same number of words in each document has not practically described the characteristics of plant taxonomy. The evaluation research of classification algorithm Probalistic Neural Network (PNN) with weight value 1 and layer patterns using feature extraction using the chi-square values of alpha (α) value of 0.1 overall result of classification is 82.14%. This research represents a medical plant document system that can be used for automatic identification of families according to the taxonomy of plants.This system is useful to help users especially researchers and taxonomists in the identification document through labeling family automatically on each document, with the existing system, therefoe the users dont need to bring a book or a guide book to identify the field. Furthermore, it can overcome the limitations of people's knowledge of the diversity and the use of medicinal plants. That is because there are a lot of documents to train the same word in each document which is not characteristic of plant taxonomy.
Keyword: probalistic neural network classifier, medical plant document, document identification, document classification, chi-square

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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