Muharni, Yusraini and Kulsum, Kulsum and Hartono, Hartono The application of artificial neural network for quality prediction of industrial standard water. IOP Conference Series: Earth and Environmental Science (EES). ISSN 17551307, 17551315 (In Press)

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Abstract

Industrial Standard water is a source of water before being distributed in industry and household in a certain area. For the sake of health, quality water is a must to fulfill and control. Quality of water having several variables as indicators. One indicator, namely, Turbidity. It is defined as the level of cloudiness of water due to the presence of particles, mud or microorganisms. The highest of turbidity value meaning the index of water quality is low. In this study, we apply the Artificial neural network method for predicting the turbidity value. Three input variables are engaged, PH level, color spectrum, and electrical conductivity. As much of 827 data were collected during six months. Seventy percent are used for training and the rest thirty percent were used for testing. The ANN architecture consists of 3-6-1 configuration, 3 input variables, 6 hidden layers, and 1 output variable. The training was set into 1000 epoch and the MSE shows 0,0013, meaning that the ANN has the power of prediction. The prediction of turbidity level has a managerial implication as supporting information for purchasing decision of material in water processing

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