Judul: Pengenalan Wajah dengan Citra Pelatihan Tunggal Menggunakan Algoritme VFI5 Berbasis Histogram
Abstrak: Face recognition is one of the biometric methods which is frequently used. One of the approach in face recognition is view based (photometric) approach which represents the information of the image pixels. In this research, information from the image pixels represented using the image histogram. According to Gonzales (2002), histogram are the basis for numerous spatial domain processing techniques. Histogram is used as input to Voting Feature Intervals 5 (VFI5) algorithm. Range of image histogram is 0-255 divided into several intervals. Afterwards, on training stage will be determined the vote value from single training image per person that will be used in the classification stage. The recognition rate is reached 96.3%.
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Judul: Pengenalan wajah dengan partisi citra menggunakan VFI5 berbasis histogram
Abstrak: Research in face recognition is motivated not only by the fundamental challenges this recognition problem poses but also by numerous practical applications where human identification is needed. Face recognition, as one of the primary biometric technologies, became more important owing to rapid advances in technologies such as digital cameras, the Internet and mobile devices, and increased demands on security. Face recognition has several advantages over other biometric technologies: It is natural, non intrusive, and easy to use. There are two predominant approaches to the face recognition problem: geometric (feature based) and photometric (view based). A classification algorithm, called VFI5 (Voting Feature Intervals), is developed and applied to problem of face recognition with partition based on histogram. VFI5 represents a concept in the form of feature intervals on each feature dimension separately. Classification in the VFI5 algorithm is based on a real-valued voting. Each feature equally participates in the voting process and the class that receives the maximum amount of votes is declared to be the predicted class. The performance of the VFI5 classifier is evaluated empirically in terms of classification accuracy. Conclusion of this research indicates that VFI5 can be used for face recognition with high accuracy the level.
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Judul: Bioactivity of Essential Oil from Suren (Toona sinensis Roemor) Based on the Brine Shrimp Lethality Test (BSLT)
Abstrak: INTRODUCTION. This study to determine the yield of essential oil from the leaves, heart wood, sap wood and inner bark, and also to determine the bioactivity of essential oil from suren (Toona sinensis Roemor). E ANALYSIS AND METHOD. ssential oil of suren leaves have been tested for use as insecticide. Because of that, essential oil of suren are interesting to studied. Parts of trees that used are the leaves, inner bark, heart wood and sap wood. To generate the yield, the raw materials are destilated using method steam and water. As for determining bioactivity conducted tests on shrimp larvae Artimia salina Leach. through the Brine Shrimp Lethality Test methods. Thin Layer Chromatography Test (TLC) was conducted to estimate the number of compounds contained in the essential oil RESULT AND DISCUSSION. . The results showed that yield of the sap wood is (0.388%), heart wood (0.379%), leaves (0.302%), and inner bark (0.051%), which yield greatest value in the sap wood. The results of bioactivity tests showed LC50 values at the leaf essential oil (11.203 μg/mL), inner bark (6.851 μg/mL), heart wood (3.968 μg/mL), and sap wood (1.293 μg/mL). These results indicate that all essential oils are bioactive against A. salina and sapwood essential oils have the highest bioactivity effect. The results of the analysis by TLC showed that essential oils from various parts of the tree is thought to contain terpenoid compounds. Keyword: Surianwood, cederwood, bioactivity, Artemia salina.
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