Judul: Penerapan Backpropagation Neural Network Untuk Peramalan Penjualan Produk Susu
Abstrak: Milk product developed very rapidly nowadays. This is so many brand comes up to the market and makes competition between those products harder than before. Therefore each company must have analytical plan to predict how many product should be produced in order to avoid over product or lack of product and in order to keep distribution process run smoothly. Artificial neural network is one of machinal learning approach that can be used to solve so many problems primarily a complex problem than can not be modeled. Forecasting data time series is one of type of problem that can be solved by this approach. The objective of this research is to forecast milk product sale in KPBS Pangalengan, Bandung. This research consist of five steps such as problem identification, data collection and field information, preprocessing, neural network model, and postprocessing. This research use implementation of artificial neural network forecasting to predict milk product variant cup flavor in KPBS, Pangalengan Bandung. Artificial neural network consist of some composition data of milk product sale during January 2008-Juni 2010 that already trained based on work principle of human brain. Model of artificial neural network that used is this research is backpropagation neural network with two hidden layer and one output. Artificial neural network with one layer has a restriction in pattern recognition. This restriction can be solved by adding one or some hidden layer between input and output layer. Backpropagation is a model of artificial neural network with plural layer. Like other artificial neural network, backpropagation trains the network to balance its capability to recognize pattern and to give appropriate response to input pattern that similar to the pattern that used in training session. After training and examination, validation was done to evaluate the performance of artificial neural network in recognizing actual data pattern and the result shows that 4 data (67%) is appropriate to the target (actual data) and 2 data (33%) is not appropriate. This result shows that error in examination process is low. Mean square error (MSE) of validation process is 0.0286. After further examination bigger data training, MSE changed to 0.083. This MSE is bigger than the previous result. In addition, artificial neural network in this further research shows only 52% data that appropriate with target and 48% is not appropriate with real data. This result is affected by some factors. One of them is data that used in training and examination phase is random data between maximum level and minimum level of each attribute. The result shows that implementation of artificial neural network can help industrial sector to anticipate raw material and product stock accurately.
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Judul: Sistem Intelijen Penilaian dan Prediksi Kualitas Susu Pasteurisasi (Studi Kasus pada PT . Industri Susu Alam Murni Bandung)
Abstrak: The aim of this research is developing an intelligent system for assessment and prediction of pasteurized milk quality. The system is implemented by integrated expert systems and Artificial Neural Network (ANN). This system is designed and developed by Matlab 6.5 and called SINKUAL-SP. The logical strategy model used is “Forward Chaining” and the tracing method used is “Best First Search” method. The method of Certainty Factor (CF) is used for handling uncertainty. Model Based Management system consist of five model : quality of fresh milk model, quality of process model, quality packaging and storage model, statistical process control model and prediction model by neural network. Multilayer system is used for the neural network architecture with one input layer, one hidden layer dan one output layer. The activation function is Sigmoid Bipolar activation function which gives the best performance network with learning rate 0.005 and momentum 0.9 together with RMSE, MSE and SSE as criterion error. The validation for neural network indicates the conformity between output of neural network and output of goal with value of RMSE 0.009959, MSE 0.00999 and SSE 0.009950. Decision for acceptance fresh milk and pasteurized of milk is implemented by acceptance grade which gives control quality process is easier than previously system. Based on the grade acceptance, the management of industry can take decision and action allow with a significant output of grade. In the research implementation stage, the data, gained in the company of “PT. ISAM” in April 2005, brings a significant output of research regarding quality of fresh milk such as the grade B, grade A for quality of process, and grade B for quality packaging and storage. This system gives a suggestion for user to improve the quality of pasteurized milk. Based on the outputs from the systems used, highly priority strategy quality system reconstruction, with 0.236 as weighting value. The quality improvement system upon the results of research is seemed to be a way of improving the quality of milk in process of pasteurization.
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Judul: Sintesis dan Pencirian Kompleks Fe(III)-Kuersetin
Abstrak: Quercetin belongs to flavonols which is able to chelate metal ions. Quercetin may chelate metal ions at 3’,4’-catechol, 3- or 5-hydroxycromone sites. In this study, quercetin was complexed with Fe(III). The complex with Fe(III) ions from FeCl3·6H2O was more stable than the complex derived from Fe(NO3)3·9H2O. The quercetin was yellow in color and showed maximum wavelengths (λ􀭫􀭟􀭶) at 250 and 370 nm. The Fe(III)-quercetin complex from Fe(NO3)3·9H2O was yellowish green with 􀟣􀭫􀭟􀭶 at 280 and 420 nm, whereas that from FeCl3·6H2O was dark green with 􀟣􀭫􀭟􀭶 at 280 and 430 nm. The 􀟣􀭫􀭟􀭶 shift indicated that the complex was formed at 3-hydroxychromone site. The Fe(III)- quercetin complex was formed with 1:1 metal-ligand mole ratio based on atomic absorption spectrophotometry analysis and the X-ray diffractogram showed that the crystallinity of the complex was 37%.
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