Judul: Feature Extraction Based on Color, Shape and Texture for Animal Image Retrieval.
Abstrak: The differences in color, shape, and texture characteristics between each animal cause difficulties in animal image retrieval. Therefore, specified technique is needed to obtain pattern from each animal’s feature so that image retrieval might works properly. One of the techniques to obtain the pattern done by feature extraction. This research tries to develop image extraction method in Content Based Image Retrieval approach by using three image visual features (color, shape, texture). In this research, Fuzzy Color Histogram (FCH) for color feature extraction is used through computing the membership function using Cauchy function. In FCH, one color may belong to two bins histogram or more with different membership functions. Then, shape feature is extracted using edge direction histogram, where each image is processed using Sobel edge detection, and then the direction is mapped into a defined bin histogram. For texture feature extraction, the process uses co-occurrence matrix with computing the values of energy, moment, entropy, maximum probability, contrast, correlation and homogeneity. Similarity value between image query and images in database is computed based on its features, those are color, shape, texture, and its combination. Recall and precision value resulted in this research shows the largest average precision value is obtained from searching process using index combination (color, shape, texture) of feature extraction. Keywords: feature extraction, Fuzzy Color Histogram, edge direction histogram, co-occurrence matrix
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Judul: Morphology and Texture Features Extraction for Leaf Sheet Image Retrieval.
Abstrak: In the biology and forestry science, there are many kinds of leaves with different features. It causes difficulty to make leaf identification. Colour feature is not too significantly different because generally they are green. So, the primary leaf identification depends on morphology and texture feature. The two of them makes leaf identification more accurate. But, the combination makes some varies. A lot of people even the scientist are difficult to recognize the leaf, especially in leaf image. The reason to make feature extraction automatically by computer will be used for leaf content based image retrieval. This research started with collected leaf images by taking some pictures of them with digital camera. The collected location was Bogor Agricultural University Darmaga area. Eleven kinds of fruit tree leaves were photoing, one hundred pictures for each kind. So, there are eleven classes of leaf on database. The next step was cleaning the images. Background images were cleaned to get white background. After that, Digital Morphological Features Extraction was used to extract the shape features and Cooccurrence Matrix for texture features. Three primary features of shape are extracted and calculated it to get three derivative features. Meanwhile, seven features of texture are extracted with Coocurrence Matrix. Those features were combined with Bayesian Network Model to improve content based image retrieval. Generally, Bayesian Network Model increases precision value of image retrieval. Average precision value for Bayesian Network Model on all class is 0.2733. This is the highest value. Average precision value using texture feature on all class is 0.2722. Then, average precision value using shape feature on all classes is 0.2531. This is the lowest value. Keyword : Content Based Image Retrieval (CBIR), feature extraction, Morphological Features Extraction, Co-occurrence Matrix, Bayesian Netrwork Model.
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Judul: Penghambatan Degradasi Sukrosa Dalam Nira Tebu Menggunakan Gelembung Gas Nitrogen Dalam Reaktor Venturi Bersirkulasi
Abstrak: Degradasi sukrosa yang terkandung dalam nira tebu selama pengolahan disebabkan oleh reaksi inversi enzimatis dan aktivitas mikroba. Inversi sukrosa disebabkan oleh waktu tunggu (dowmtime) karena kerusakan peralatan pada saat proses berlangsung. Proses penghambatan degradasi sukrosa dalam nira tebu dapat dilakukan dengan gelembung gas inert menggunakan reaktor venturi bersirkulasi. Penelitian ini dilakukan untuk mendapatkan teknologi penghambatan degradasi sukrosa dalam nira tebu menggunakan gelembung gas nitrogen dalam reaktor venturi bersirkulasi. Penelitian dilakukan dengan memvariasikan laju alir nira pada 25 l/min, 20 l/min, dan 15 l/min, dengan kecepatan aliran gas nitrogen antara 0,02 m/det sampai 0,6 m/det menggunakan ukuran nozel 5 mm, 6 mm, dan 8 mm. Hasil penelitian menunjukkan gas hold-up yang terbentuk sangat baik untuk penghambatan degradasi sukrosa, dan menunjukkan adanya korelasi linier antara penghambatan sukrosa dengan gas hold-up. Laju penghambatan degradasi sukrosa pada laju alir nira 25 l/min dan kecepatan aliran gas nitrogen 0,6 m/det menggunakan ukuran nozel 6 mm dan suhu reaktor 70 oC memberikan penghambatan sebesar 4,2 %. Proses penghambatan degradasi sukrosa dengan menggunakan gelembung gas nitrogen dipengaruhi oleh gas hold-up dan ukuran gelembung yang terbentuk
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