Judul: Ekstraksi ciri morfologi (panjang dan lebar fisiologis) dan tekstur untuk temu kembali citra helai daun
Abstrak: In recent time, the ability to identify and classify leaves becomes a great need for taxonomist to know the diversity of plants (Hickey et al 1999). Identification can be done by identifying features of morphology and texture of the leaves or also with a combination of both. Annisa (2009) implemented the feature extraction approach to obtain the basic characteristic morphological image derived from the leaf blade and the co-occurrence matrix for texture feature extraction. Morphological features that obtained were diameter, leaf area, leaf perimeter, smoothing factor, form factor and perimeter ratio of diameter. Texture features were obtained energy, inverse difference moment, entropy, maximum probability, contrast, correlation, and homogeneity. This study used morphological features approach to obtain morphological features and co-occurrence matrix for texture features. Therefore, the evaluation of image retrieval was done by using more complete morphological features with additional morphological features of physiological length and physiological width. Both morphological features are very useful to help identify the characteristics of a leaf blade. This research approach has been successfully implemented morphological features. Value of image retrieval evaluation leaves increased with the addition of two morphological characters. In this research, with additional morphological features and physiological length and physiological width resulted in morphological traits 0.2083, the texture feature values 0.1864 and character of Bayesian Network 0.2055. So that the average value of precision increased by 0.0137 for morphological features and by 0.0069 for combined value of morphological and textural characteristics (Bayesian Network).
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Judul: Penggabungan ciri morfologi, tekstur, dan bentuk untuk identifikasi daun menggunakan Probabilistic Neural Network
Abstrak: This research proposes a new system for leaf identification using classifier combination and probabilistic neural network. The features that used for classification are morphology, texture, and shape of leaves. Classifiers combination is used to combine these features. The method to extract texture and shape are co-occurrence matrix and Fourier descriptors, respectively. After feature extraction, the feature is classified using probabilistic neural network. The experiment data consist of thirty species flora from Bogor Botanical Garden, Indonesia. The experiment results show that classification without classifier combination has the accuracy of 79.05%, and using classifier combination the accuracy increased to 83.33%. Hence, the propose system is promising for leaf identification and supporting plant biodiversity in Indonesia..
Keyword: Leaf identification; Probabilistic Neural Network; Classifier combination, Probabilistic Neural Network, Classifier combination, 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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