Abstract:Purpose: The purpose of this research is to develop an assessment model to identify phase of industrial cluster life cycle which comprises definition of the cycle phases, identification of assessment components, and characterization of each phase of cluster life cycle.
The paper is intended to develop a model to predict the number of damaged buildings and casualties due to earthquake using ANN (Artificial Neural Network). This model is expected to be able to generate the type and amount of relief supplies required by those affected during the emergency phase. This research develops ANN using supervised learning paradigm, and backpropagation learning algorithm. The applied ANN network architecture is a multiple-layer system, with 1 (one) neuron used in both input and output layer, and 95 (ninety-five) neurons used in the hidden layer yielding 0.99971 as the greatest value of the correlation coefficient. The output variable in this study is the earthquake impact consisting of six variables. While the input variables (predictors) in this study consisting of eight variables. The model in this study utilizes 123 seismic datasets, divided into 100 data (80%) for the training process and 23 data (20%) for the testing process. This research adds to the existing research and demonstrates the application of ANN in predicting the numbers of damaged buildings and casualties. The model is useful in supporting and strengthening preparedness and emergency relief activities due to earthquake disaster.
Abstract:Purpose: The purpose of this research is to develop a model that will explain the impact of government policies to the competitiveness of palm oil industry. The model involves two commodities in this industry, namely crude palm oil (CPO) and refined palm oil (RPO), each has different added value.Design/methodology/approach: The model built will define the behavior of government in controlling palm oil industry, and their interactions with macro-environment, in order to improve the competitiveness of the industry. Therefore the first step was to map the main activities in this industry using value chain analysis. After that a conceptual model was built, where the output of the model is competitiveness of the industry based on market share. The third step was model formulation. The model is then utilized to simulate the policy mix given by government in improving the competitiveness of Palm Oil Industry.
Findings:The model can accommodate government's policy mix which is then optimized. The model has been built structurally based on hierarchical multi-level system approach, while in the process element, the subprocesses are built using VCA approach. The model can simulate industry performance, and show that such government policy mix can improve the competitiveness of Indonesian palm Oil Industry.
Research limitations/implications:The model was developed using only some policies which give direct impact to the competitiveness of the industry. For macro environment input, only price is considered in this model.-231-Journal of Industrial Engineering and Management -http://dx.doi.org/10.3926/jiem.1582 Practical implications: The model can simulate the output of the industry for various government policies mix given to the industry. The techno-economic aspect is also discussed.
Originality/value:This research develops a model that can represent the structure and relationship between industry, government and macro environment, using value chain analysis and hierarchical multilevel system approach.
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