Once a major salt producer, Indonesia has imported million tons of salt in recent years to meet domestic demands of chemical industries. Indonesia's salt-producing potential has been hindered by lack of competitiveness and unsynchronized production data. The salt supply chain process is typically finished on a monthly basis, yet the uncertainty of weather conditions often leads to erratic production yields. Since heavy reliance on the weather can bring negative consequences for salt farmers around the country, accurate salt field productivity forecasting is of great importance. This study aims at examining sunlight duration, wind speed and temperature data to predict salt field productivity in Kalianget Sumenep Madura. The predictive model is developed using Artificial Neural Network (ANN) method because it has a low risk of fault to solve nonlinear relationships. The effects of different learning rate and momentum values are analyzed by full factorial design of experiment and evaluated based on the lowest root mean square error (RMSE). Then, the optimal model is used to test and compare the forecasting performance based on ANN and Holt-Winters predictors. The result demonstrates that the proposed model is accurate and efficient to represent a good solution to predict salt field productivity in the region.
This study aims to implement decision making methodology to select raw material suppliers for sea cucumbers in fish cracker Micro, Small and Medium Enterprises (MSMEs) in Bangkalan Districts, Indonesia. Raw materials sourcing is one of the most critical functions in MSMEs because of its significant effect in reducing production costs and increasing overall profits. The sea cucumbers suppliers’ selection process is analysed in two phases. The first phase is the development of a hierarchy of problems in selecting fish cracker MSMEs suppliers to determine the selection criteria and sub-criteria. The Analytical Hierarchy Process (AHP) was used to determine pairwise comparisons, consistent weights and priority decisions of the alternative raw materials suppliers. In the second phase, Multi – Objective Optimization on The Basis of Ratio Analysis (MOORA) is conducted using the AHP results as input to optimize two or more conflicting objectives subject to some constraints. Then, the AHP method and the MOORA method were used to obtain the MSME’s sea cucumber suppliers ranking and evaluate the best possible suppliers.
In recent years, Indonesia needs import million tons of salt to satisfy domestic industries demand. The production of salt in Indonesia is highly dependent on the weather. Therefore, this article aims to develop a prediction model by examining rainfall, humidity and wind speed data to estimate salt production. In this research, Artificial Neural Network (ANN) method is used to develop a model based on data collected from Kaliumenet Sumenep Madura. The model analysis uses the full experimental factorial design to determine the effect of the ANN parameter differences. Then, the selected model performance compared with the estimate predictor of Holt-Winters. The results present that ANN-based models are more accurate and efficient for predicting salt field productivity.
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