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: Identifikasi Jenis Shorea Menggunakan Jaringan Syaraf Tiruan Propagasi Balik Resilient Berdasarkan Karakteristik Morfologi Daun
Abstrak: Shorea (Meranti) is one of the best kind of wood from tropical forests in Indonesia, and one of the best timber of the family Dipterocarpaceae. Shorea has the highest species diversity among its family, which consists about 194 species. This makes it difficult to identify the species of Shorea. Experts in the field of automatic Shorea identification is still rare. Hence, identification using the morphological characteristic of Shorea leaves is more preferred. This research is focused to identify Shorea species using morphological characteristics of leaves. The data used are manual measurement from a sample of the leaves which are taken from each type of Shorea ovalis, Shorea leprosula, Shorea platyclados, Shorea seminis, and Shorea beccariana. Sample of the leaves is taken from the Shorea collection at the Bogor Botanical Gardens. Classification model is built by using resilient backpropagation neural network. After the data has been collected, the feature values of these five species of Shorea is used in the experiment for training and testing the resilient backpropagation neural network. In the training process, we use a variation of the resilient backpropagation neural network parameters in a 5-fold cross-validation experiment. Each experiment unit is divided into two types: with regression and without regression. Overall, the accuracies of the experiment was 98%. Both types produce the same accuracy with different resilient backpropagation neural network optimal parameters, and the same incorrect identification, which is an instance of Shorea leprosula being identified as an instance of Shorea ovalis.
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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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