Judul: Pengenalan Genus Diatom Menggunakan Principal Component Analysis dan Jaringan Syaraf Tiruan Backpropagation sebagai Classifier
Abstrak: Diatoms are unicellular algae which have a size between 2 μm and 4 mm. Their importance resides in the fact that they can be used in several research and scientific fields. For instance, they can be used to measure sediment pH, medicinal, as water quality indicators, etc. The recognition and identification of diatoms is a tedious work. This classification process is complicated even for the experts, because there are hundreds of different taxa with many variations in shapes and biological characteristics. This research apply Principal Component Analysis (PCA) for data reduction and Artificial Neural Network (ANN) to identify some kind of diatoms. The proportion of PCA are used in this research is 80% and 90%. This proportion is considered to replace the original data without much loss of information. Backpropagation ANN that used is a single hidden layer. The data used in this study is a JPG image of diatom sampling using electric microscope taken from Biomikro Laboratory, The productivity and Water Environment, Department of Water Resources Management, Faculty of Fisheries and Marine Science, Bogor Agricultural University. All images are divide using two scenarios percentage. The first scenario divide 60% for training data and 40% for testing data, while the second percentage is 80% for training data and 20% of testing data. As result, ANN can be used to identify diatoms. The results showed that the best generalization rate of 90% was obtained in an experiment using 90% PCA proportion with 80% of training data and 20% of testing data.
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Judul: Identifikasi Citra Karang Menggunakan Jaringan Syaraf Tiruan: Kasus Family Pocilloporidae
Abstrak: Nowadays, the trend method for coral identification is by using the visual technique since it does not have to take the coral sample which can damage the coral growth. This technique needs visual skill and the ability to classify the patterns which are formed by the color, the texture and the shape of coral. So far, this visual technique can only be done by the expert and experienced researchers. Therefore, in this research, the implementation of backpropagation artificial neural network is done to identify the genus image of corals as the application of visual technique in computer system. Hopefully, this application can help and make it easier for the early-stage researchers to identify corals from the features of photography results. The genus which belong to the family of pocilloporidae. The feature extractions; colors,textures and shapes are used as the input to the network. The methods are RGB and HSV for color, moment invariant for shape and three methods which are statistical moment, gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP). The experiments towards the combination of color and shape features on each texture feature, show that the identification level of color, texture (statistical moment) and shape is better than other feature combinations. The result of the recognition rate is 96% and the elapsed time ìs 10.94 second. The recognition rate of color, texture (GLCM) and shape is 96% and the elapsed time is 20.849 second. While the recognition rate on color, texture (LBP) and shape is lower, that is 68% and the elapsed time is 0.465 second.
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Judul: Epivag Sebagai Penyebab Kegagalan Reproduksi Dan Penanggulangannya Pada Sapi
Abstrak: "Epivag" merupaka suatu penyakit viral yang menyerang organ reproduksi baik pada sapi jantan maupun betina, disebabkan oleh virus dan hanya terdapat di benua Afrika. Nampaknya sapi-sapi sialng Inggris di Afrika sangant peka terhadap penyakit tersebut. Mengingat bahwa sapi-sapi silang Inggris sudah banyak di impor ke Indonesia, maka perlu diperhitungkan kemungkinan penyebaran penyakit tersebut di Indonesia.
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