Judul: Optimasi jaringan syaraf tiruan menggunakan algoritme genetika
Abstrak: Neural network is one of common computational model to recognize pattern. Backpropagation algorithm can be used as method to train the networks weights. One of the drawbacks of backpropagation training is the initial weights generated randomly. It causes the instability of neural network training performance. Because of this, many experiment and training must be conducted to obtain the desired model. This research combine genetic algorithm and neural network to overcome random generation problem. Genetic Algorithm (GA) is used to find the optimal initial weights based on learning using Mean Square Error as objective function. The initial weights using GA is expected to be stable and better than those generated. The experimental research showed that genetic algorithm can be used to optimize the performance of backpropagation neural network. The optimal initial weights from genetic algorithm is more stable than the initial weights from regular method. The classification accuracy of genetic algortihm and neural network is also higher than regular method. This indicated GA method is promising to increase the neural network performance and can be applied in various real problem.
Keyword: neural network, genetic algorithm, optimation, backpropagation, classification

Judul: Jaringan Syaraf Tiruan dan Algoritma Genetika dalam Pemodelan Kalibrasi (Studi Kasus: Tanaman Obat Temulawak)
Abstrak: The problems in prediction of calibration model are multicolinearity and the number of variables is larger than the number of observations. Principal Component Analysis-Artificial Neural Network-Genetic Algorithm (PCA-ANNGA) models were applied for the relationship between sample of concentration which is limited and transmittance data which is in large dimensions. A large number of variables were compressed into principal components (PC’s). From these PC’s, the ANN was employed for prediction of concentration. The principal components computed by PCA were applied as inputs to a backpropagation neural network with one hidden layer. The models was evaluated using GA for the best network structure on hidden layer. Root Mean Square Error (RMSE) for 80% training set and 20% testing set are 0.0314 and 0.5225, respectively. Distribution of data according to the percentage of training data and testing data were also very influential to obtain the best network structure with the smallest RMSE achievement. The best model for these methods is two layers Neural Network with eight neuron in the hidden layer.
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Judul: Insecticidal Activity of Essential Oils Leaves of Cinnamomum spp. (Lauraceae) against Crocidolomia pavonana and Their Phytotoxicity on Broccoli Seedlings
Abstrak: Essential oils from leaves of 13 species of Cinnamomum were tested for their insecticidal activity on the cabbage head caterpillar Crocidolomia pavonana with leaf immerse method. The results showed that the treatment with 1% (w/v) essential oils leaves of C. celebicum, C. multiflorum, C. sintoc, and C. verum caused 21.45, 93.65, 60.30, and 76.10% mortality, respectively. Based on the results of phytotoxicity and insecticidal activity tests, C. multiflorum oil was further tested at 6 concentration levels. In all treatments, larval mortality has occurred at 24 hours after treatment (HAT) and increased at 48 HAT. LC50 of C. multiflorum oil at 24 and 48 HAT was 0.504 and 0.396%, respectively. The treatment with C. multiflorum oil at ≤0.31% did not inhibit the development of C. pavonana larvae. Determination of the chemical constituents of Cinnamomum oils were carried out by gas chromatography-mass spectrometry (GCMS). Totally, 345 compounds were identified. Methyleugenol constituted the main component (49.4%) of leaf essential oil of C. multiflorum and was assumed to be responsible for the insecticidal activity of the oil against C. pavonana larvae. LC50 of pure methyleugenol obtained at 0.365% (48 HAT) and 0.363% (72 HAT). The results showed that C. multiflorum was more effective with LC50 value equivalent to methyleugenol content of 0.32% (24 HAT) and 0.24% (48 HAT).
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