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: Pengaruh Program Pemberian Tablet Tambah Darah (TTD) Terhadap Perbaikan Status Besi pada Remaja Putri di Kota Bogor
Abstrak: Anemia merupakan masalah gizi mikro yang banyak terjadi di seluruh dunia terutama di negara berkembang yang diperkirakan terjadi pada 30% populasi penduduk dunia. Sebesar 29% Wanita Usia Subur (WUS) yang tidak hamil mengalami anemia serta 38% wanita hamil usia 15-49 tahun mengalami anemia (WHO 2011). Anemia pada remaja putri sampai saat ini masih cukup tinggi. Data Kemenkes (2013) menunjukkan prevalensi anemia gizi pada kelompok usia remaja (≥15 tahun) adalah 22.2%, meningkat dari data Kemenkes tahun 2007 yakni 19.7% terkait kejadian anemia pada usia remaja (Kemenkes RI 2007; Kemenkes RI 2013). ...
Keyword: anemia, efektivitas, remaja putri, suplementasi besi