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: Ekstraksi ciri morfologi (panjang dan lebar fisiologis) dan tekstur untuk temu kembali citra helai daun
Abstrak: In recent time, the ability to identify and classify leaves becomes a great need for taxonomist to know the diversity of plants (Hickey et al 1999). Identification can be done by identifying features of morphology and texture of the leaves or also with a combination of both. Annisa (2009) implemented the feature extraction approach to obtain the basic characteristic morphological image derived from the leaf blade and the co-occurrence matrix for texture feature extraction. Morphological features that obtained were diameter, leaf area, leaf perimeter, smoothing factor, form factor and perimeter ratio of diameter. Texture features were obtained energy, inverse difference moment, entropy, maximum probability, contrast, correlation, and homogeneity. This study used morphological features approach to obtain morphological features and co-occurrence matrix for texture features. Therefore, the evaluation of image retrieval was done by using more complete morphological features with additional morphological features of physiological length and physiological width. Both morphological features are very useful to help identify the characteristics of a leaf blade. This research approach has been successfully implemented morphological features. Value of image retrieval evaluation leaves increased with the addition of two morphological characters. In this research, with additional morphological features and physiological length and physiological width resulted in morphological traits 0.2083, the texture feature values 0.1864 and character of Bayesian Network 0.2055. So that the average value of precision increased by 0.0137 for morphological features and by 0.0069 for combined value of morphological and textural characteristics (Bayesian Network).
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Judul: Konsumsi Pangan dan Status Gizi Ibu Hamil di Kecamatan Danurejan, Kodya Yogyakarta, Daerah Istimewa Yogyakarta
Abstrak: Hasil penelitian menunjukkan bahwa rata-rata konsumsi energi per orang per hari adalah 1 525 Kalori, konsumsi protein 45,l g, konsumsi zat besi 12,8 mg dan konsumsi vitamin A 2 264 SI. Rata-rata kecukupan energi per orang per hari adalah 2 213 Kalori, kecukupan protein 75,6 g, kecukupan zat besi 29,O mg dan kecukupan vitamin A 4 500 SI.
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