Judul: Ekstraksi Tekstur Citra Menggunakan Gaussian dan Multi-Block Local Binary Pattern untuk Identifikasi Tumbuhan Obat
Abstrak: Plants identification automatically is still a uses for recognizing various kinds of house plant species and medicinal plants. This research uses a method Multi-Block Local Binary Pattern (MBLBP) descriptor to extract texture feature and Probabilistic Neural Network (PNN) classifying for identifying a house plants and medicinal plants automatically. There are three kinds of MBLBP descriptor used in this research, i.e., , , , , and , . For training and testing, this research uses database of 1440 medicinal plant leaf images and 300 tree images belonging to 30 different types and obtained from Biofarmaka IPB, Cikabayan Farm, Green house Center Ex- Situ Conservation of Medicinal Plant Indonesia Tropical Forest, and Gunung Leutik. The experimental result shows that the concatination of , has the best accuracy in identifying house plants with an accuracy of 77.78%. It shows that MBLBP method is better than LBP method in identifying house plants based on the increase accuracy by 4.45%.
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Judul: Identifikasi tanaman hias secara otomatis menggunakan Metode Local Binary Patterns descriptor dan Probabilistic Neural Network
Abstrak: Identification house plant species automatically still be a problem for recognizing various kind of house plants. This research proposes a new method for identifying a house plant using Local Binary Patterns (LBP) descriptor and Probabilistic Neural Network (PNN). There are three kinds of LBP descriptor used in this research, i.e. , , and . Database of 300 house plant images belong to 30 different types of house plant in Indonesia are extracted using , , and based on texture feature and classified using PNN. The experimental result shows that has the best accuracy to indentify house plants with accuracy 73.33%. The proposed system is promising since it is capable to identify house plants species efficienty and accurately
Keyword: Plant extraction, Local binary patterns, Texture feature, Probabilistic neural network, Bogor Agricultural University, Institut Pertanian Bogor, IPB

Judul: Pengaruh vaksinasi terhadap dinamika populasi pada model SIR (Susceptible-Infected-Recovered)
Abstrak: SIR model can be used to model the spread of diseases in a closed population based on certain assumptions. In this research, the SIR model is formulated by including the factor of vaccination. As the result, the model has been shown to have two equilibrium points, i.e. disease free and endemic equilibrium. A simulation study is carried out to illustrate the model. The results show that some equilibrium points are asymptotically stable. Moreover, a minimum level of vaccination has been defined. For the vaccination level less than the minimum level, increase in the vaccination level results in decrease of the proportion of infected individuals in the corresponding equilibrium point. On the other hand, for the vaccination level greater than the minimum level, increase in the vaccination level results in decrease the proportion of susceptible individuals in the corresponding equilibrium point. Furthermore, when vaccination level is greater than the minimum level, it is found that the time needed for the disease to extinct is shorter.
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