Judul: Clustering Optimization using Particle Swarm Optimization for Medicinal Plant Identification System based on Digital Image
Abstrak: Indonesia is a country with a high level of biodiversity and there are 22,500 species of medicinal plants. The percentage of medicinal plant used by Indonesian people is only 4.4% or as much as 1,000 species. The people’s lack of knowledge becomes one of the cause of this low use of medicinal plant in Indonesia. People’s insight about medicinal plant can be increased by developing an identification system of medicinal plant. In this research, we used fuzzy local binary pattern (FLBP) to extract the feature. The number of digital image that used in this research are 1,440 leaf image. Unsupervised learning technique known as clustering is proposed to compare the results of the identification of medicinal plants with classification techniques. Clustering methods used is the k-means clustering and fuzzy c-means clustering. The disadvantages of both method is that the results are sensitive to the selection of initial cluster centers and the calculation of local solutions to achieve optimal conditions. This research applied a particle swarm optimization (PSO) method to overcome the weaknesses of k-means clustering and fuzzy c-means clustering. The application of PSO method in the identification system as a purpose to improve the identification of medicinal plants. This research builds four models of clustering is the k-means clustering, fuzzy c-means clustering, PSO based k-means clustering and PSO-based fuzzy c-means clustering. The development of clustering model using C++ programming language. Evaluation model of clustering using an accuracy value indicates that the PSO method can improve the identification of the k-means clustering and fuzzy c-means clustering. Accuracy of k-means clustering resulted 41.67% and PSO-based k-means clustering is 48%. Accuracy of fuzzy c-means clustering resulted in 50% and PSO based fuzzy c-means clustering is 52.33%. Evaluation of clustering models using computational time indicates the addition of time on the PSO method. The addition of algorithms led to growing number of data processing. Computation time on k-means clustering 291.3 seconds and PSO based k-means clustering 403.3 seconds. Computation time on fuzzy c-means clustering 59.65 seconds and PSO based fuzzy c-means clustering 60.1 seconds. PSO based fuzzy c-means clustering is a better method for generating high accuracy and faster computing time.
Keyword: Fuzzy C-Means Clustering, Fuzzy Local Binary Pattern, K-Means Clustering, Particle Swarm Optimization.

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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