This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬-insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR.
This paper was presented Artificial Neural Network (ANN) as one of the predicting methods to obtain more accurate predicted data. Several methods have been applied to this purpose but still not gave a better accuracy. The method could be used in linear and nonlinear characteristic of data. Back propagation neural network was implemented in this experiment to solve the predicting problem. This research proposed to predict some future points to get the advantages from it. The data was demonstrated in generate from determinant of cement demand in Indonesia region. The data was used 6 variables which influenced the demand factors. The contribution of this experiment was an exploring the accuracy of predicted and the future points value of data. The result of this experiment was: data A, B, C, D, E and F data had the range of MSE 6.23e-9 until 1.34e-7. The MSE for the actual data was 2.39e-6 and the predicted was 1.99e-6. The predicting points has resulted on months 91 until 97 were 0.5854, 0.8448, 0.510, 0.6462, 0.8528, 0.516 and 0.5074 respectively. The delta between predicted and actual data were 0
Purpose-The purpose of this study is to explore the concept of the economic lot sizing and the time cycle period of reordering. The stochastic demand is quite common in the real environment of a cement retailer. The study compares three methods to obtain the optimal solution of a lot-sizing ordering from the real case of the previous study where the dataset is collected from the area of some retailers at Banda Aceh Province of Indonesia. Design/Methodology/Approach-The problem model appears when the retailer with shortage has to fulfill the lot size in the optimal condition to the stochastic demand while at the same time has the backlog condition. Moreover, when the backorder needs the time horizon for replenishment where this condition influences the holding cost at the store, many retailers try to solve this problem to minimize the holding cost, but on the other side, it should fulfill the customer demand. Three methods are explored to identify that condition: a Wagner-Whitin algorithm, the Silver-Meal heuristic, and the holding and ordering costs. The three methods are applied to the lot sizing when there is a backlog. Findings-The results of this study show that the Wagner-Whitin algorithm outperforms the other two methods. It shows that the performance increases around 27% when compared to the two other methods in this study. Research Limitations/Implications-All models are almost approximate and useful to determine the cycle period on stochastic demand. Practical Implications-The calculation of the dataset with the three methods would give the simple example to the retailer when he faces the uncertainty demand models. The prediction of the calculation is done accurately than the constant calculation, which is more economic. Social Implications-The calculation will contribute to much better predictions in many cases of uncertainty.
The problem in management process and production of Aceh beef cattle farms in Aceh Besar has not been explored. This study aimed to determine the basic system of supply chain for the Aceh beef cattle production in Central Aceh Besar developed a model for optimizing the supply chain management and sustainability to increase productivity and business efficiency. This research used SWOT analysis and industrial supply chain approaches. The results showed that the current supply chain system of the Aceh beef cattle industry in Aceh Besar which has been running so far, needs to be strengthened to increase production and population of Aceh beef cattle in the future. There were 4 issues were identified: time, 29.6% faster than the current supply chain supply time; method, 60% no longer needed a business intermediary; cost, 21.4% of the live weight price of cattle was cheaper than the live weight price of current supply chain cattle; and stages, 30.8% shorter than the ongoing supply chain stages. The result of the SWOT analysis matrix showed that the SO (strength-opportunities) strategy was the main strategy for business developing of Aceh beef cattle in Central Aceh. In conclusion, it is necessary to optimize the implementation of the supply chain of Aceh Cattle Industry at Central Aceh by utilizing its strengths and suppressing the existing weaknesses from the breeding production to marketing process.
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