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 Dimensi Fraktal dan Kode Fraktal untuk Identifikasi Tumbuhan Obat Indonesia Menggunakan Probabilistic Neural Network
Abstrak: This research proposed a new method to identify Indonesia medicinal plants using fractal dimension (FD) and fractal code (FC) using Probabilistic Neural Network (PNN). This research investigates the effectiveness of fusion of FD and FC in order to identify Indonesia medicinal plants. FD is measured by Box Counting method. FD has non-integer value that represents the self-similarity of fractal. FC is based on the self-similarity in a picture. It means that small pieces of the picture can be approximated by transformed versions of some other (larger) pieces of the picture. The fusion of FD and FC is done by using vector fusion and Product Decision Rule (PDR). The total medicinal plants used in this research are 20 species taken from Biofarmaka Farm, Cikabayan and Green House Center Ex-Situ Conservation of Medicinal Plants Indonesia Tropical Forest. Each species consists of 30 images, thus the total images used in this research are 600 images. The PNN is trained using 450 images to classify 20 kinds of plants. The experimental results shows that fractal dimension has an accuracy rate of 57%, fractal code 21%, vector fusion 58%, and PDR 58%. The experimental result also shows that fractal fusion is not necessary because the fractal dimension has significant information compared to the fractal code.
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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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