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: Klasifikasi Freycinetia Berbasis Citra Anatomi Stomata Menggunakan K-Nearest Neighbor dan Jaringan Syaraf Tiruan
Abstrak: The classification of Freycinetia is conducted to portray the potential result and function of nutfah plasma spreader. This research is purposed to classify a variety of Freycinetia based on stomata anatomic image by K-Nearest Neighbor (K-NN) and Artificial Neural Network (ANN). The Variety of Freycinetia classification can be described by using two different ways that are morphology and anatomic characteristic. Stomata anatomic image is used to support morphology characteristic in the identification process, especially in uncompleted morphology image specimen and sample. There are two feature extraction techniques in this research. First, is to pick up the color and gray scale element in the image. The scale element has a red, green and blue color (RGB). The grayscale has an entropy, contrast, energy, homogeneity, gray scale and deviation standard. Second, is a wavelet decomposition technique which has a function for decreasing a size of the image without loosing its important element. The taken element becomes a coefficient image of w-entropy for each wavelet level. Ninety six data is used in this research, it contains of four kinds of Freycinetia. They are Freycinetia angustifolia, Freycinetia imbricata, Freycinetia javanica, and Freycinetia sumatrana. The result shows an accuracy level for K-NN according to RGB and gray scale is 86.46% and according to wavelet decomposition is 96.88%. Whereas, the accuracy level of ANN based on RGB color and gray scale element is 94.79% and according to wavelet decomposition is 9 9%. The classification result of plant variety, especially Freycinetia based on stomata image with high accuracy, will be an alternative tool to identify taxonomy type.
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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