Judul: Identifikasi daun Shorea dengan Backpropagation Neural Network menggunakan ekstraksi fitur Discrete Wavelet Transform dan Ekstraksi Warna HSV
Abstrak: Dipterocarpaceae is a major timber tree of tropical rain forest. Shorea is a genus of the Dipterocarpaceae family which consists of around 194 species. Shorea is difficult to be identified because it has a lot of diversity. It takes knowledge from an expert in the field of Shorea to be able to identify the types of Shorea. Errors in identifying the type of Shorea wood can lead to inappropriate selection for the final usability. In this research, we perform identification to 10 species of Shorea from Bogor Botanical Garden using Discrete Wavelet Transform and HSV color extraction as the feature extraction methods. Backpropagation Neural Network is used as the classification technique. The results of this research using both the DWT Haar family and HSV for feature extraction, and the DWT Daubechies 2 family and HSV produce 90% accuracy. The combination between DWT Haar family, DWT Daubechies 2 family and HSV color extraction produces 93.33% accuracy. The conclusion from the results of this research is the significant effect of HSV color extraction in increasing the accuracy for the identification of Shorea leaves.
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Judul: Identifikasi jenis shorea menggunakan jaringan syaraf tiruan propagasi balik berdasarkan karakteristik morfologi daun
Abstrak: Dipterocarpaceae is a major timber tree of tropical rain forest. Shorea is a genus of the Dipterocarpaceae family which consists of around 194 species. Species diversity may lead to difficulty in distinguishing species of Shorea of one another. It takes knowledge from an expert in the field of Shorea to be able to identify the types of Shorea. Errors in identifying the type of Shorea wood can lead to inappropriate selection for the final usability. Identifying the type of Shorea carried out on five species of Shorea owned Bogor Botanical Gardens on the basis of morphological characteristics of leaves. The identification was carried out using Backpropagation Neural Network. To obtain the values of each leaf morphological characteristics of Shorea, each sample measurement data collected manually. The values are then processed using Backpropagation Neural Network to get the pattern from the training and the accuracy of the test phase. This research used a total of 50 data from five species of Shorea. The data are divided into five subsets. The fifth subset is used in the training and testing phases and conducted five times. The use of methods Backpropagation neural networks in identifying species Shorea produces an average accuracy of 90%.
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Judul: The Control And Eradication Of Brucellosis In Cattle From The Standpoint Of Human Health
Abstrak: Brucella abortus occurs both in man and cattle. The organism may thrive wherever cattle are raised by man for breeding purposes. As such, spread to humans can easily be lessened if the disease is properly controlled and/or eradicated in cattle. Control of Brucella abortus in cattle can be under taken by continued vaccination, proper attention to hygiene and the particular care taken in the introduction of only brucellosis-free animals into cattle farms. Vac- cination using the strain 19 or strain 45/20 Brucella abortus vaccine is commonly used, though the former is more popular. Each of these vaccine strains has its own advantages and disadvantages. Eradication of Brucella abortus by the test and slaughter policy can also be carried out after proper vaccination programmes have been initiated and a stage reached where only a very small percentage of positive reactors is found,
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