| dc.description.abstract | The bakery industry is growing, and there is a bakery business that provides a
place to enjoy the bakery products that they produce directly at home. Aroma
Bakery is one of the producers and bakeries in the city of Medan under the
shadow of PT. Arma Anugrah Abadi, which has several branches that continue to
grow in the province of North Sumatra to Aceh. The company's bread sales have
fluctuated, making it difficult for the company to predict the sale of bread. As a
result of errors in predicting the sale, the company has suffered some losses, such
as unworthy bread after three days, waste of raw materials, labor, and many
other losses. Therefore, this study aims to predict bread sales and identify the rate
of error of the bread sale prediction using the Extreme Learning Machine (ELM)
method based on the Mean Square Error (MSE) calculation at Aroma Bakery. The
Extreme Learning Machine method is used to forecast the sale of bread at Aroma
Bakery. Normalized data is divided into two parts: 80% for training data and
20% for testing data. Through experimental tests using 8 neurons, we obtained an
MSE value of 0.27402. Using 5 neurons, I received an MSE of 0.28761. Based on
the test results, at 5, neurons produced the smallest error value. Predicting the
number of neurons on hidden layers is done 10 times, with a total of 101 data
points for each type of bread. The biggest sale of bread was in December, with
sales of 198,850 pcs | en_US |
