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: 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: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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