<![CDATA[KHI Pipe Industries is a company that specializes in producing high-quality steel pipes. This company produces its end product based on customer demand, with the measurement specifications which is diameter, thickness and the pipe length. In the production process, the amount of viable pipes do not always match with the number of customers demand since there were always a number of damaged pipes. Therefore, the company has always have to spend additional cost to cover the the damaged pipes. The number of production on each specifications varies so that it becomes a challenge for the company to predict the exact amount of pipes to produce. With the appropriate prediction of the number of pipes to produce can help the company to determine the production target. In this research applied method of Artificial Neural Network (ANN) that is Extreme Learning Machine (ELM) to predict the amount of approved pipe production. The prediction process is normalization, training, testing, and denormalization, and to calculate the error value using Mean Square Error (MSE). Based on evaluation performed, the use of 7 hidden neurons, 5 features, and percentage comparison 80% of training data 20% of testing data resulted in the smallest error average is 0,00372 with difference ± 1% to actual data.]]>
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