Judul: Perbandingan Metode Ekstraksi Ciri Histogram dan PCA dalam Mendeteksi Stomata pada Citra Penampang Daun Freycinetia
Abstrak: Feature extraction is the process of taking an object identifier which can describe the characteristics of the object. In this study, we use two feature extraction methods, namely Histogram and PCA (Principal Component Analysis) to be used in stomata detection from the Freycinetia sectional leaves images. We used a moving frame to detect the occurrence of stomata in an image. To create a classification model, we distinguish between three frame classes: frames showing full stomata, frames showing parts of stomata, and frames which contain no part of stomata at all. For the classification method, we use the Backpropagation Artificial Neural Network for the classifier. The results of the detection process using Histogram as the feature extraction method will be compared with the results of the detection process using PCA. The best results between the two methods of feature extraction will be used as the first step in the process of species type identification for the genus Freycinetia. The research results show that the PCA feature extraction method is better than the Histogram feature extraction method in detecting the occurrence of stomata on the Freycinetia sectional leaves images. The best f1-measure value that can be achieved by the PCA feature extraction methods is 0.9091.
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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: The Addition of Niobium (Nb) to Uranium Zirconium (U-Zr) Alloy for Nuclear Fuel
Abstrak: Pengembangan bahan bakar nuklir merupakan aspek penting dalam teknologi reaktor nuklir, dengan fokus pada keamanan dan ekonomi. Saat ini, reaktor riset G.A. Siwabessy menggunakan bahan bakar uranium silisida (U3Si2/Al) yang memiliki beberapa kekurangan, seperti densitas maksimum 4,80 gU/cm, The development of nuclear fuel is a crucial aspect of nuclear reactor technology, focusing on safety and economics. Currently, the G.A. Siwabessy research reactor uses uranium silicide (U3Si2/Al) fuel, which has drawbacks, such as a maximum density of 4.80 gU/cm
Keyword: zicronium, reactor, niobium, nuclear, ?-uranium