Implementasi Metode Extreme Learning Machine (ELM) untuk Memprediksikan Penjualan Roti (Studi Kasus : Harum Bakery) - Luqman Hakim Harum; Nurul Hidayat; Ratih Kartika Dewi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Harum Bakery is a bread store located in Malang Regency area. The number of bread sales in this company is uncertain everyday. It makes the company difficult to predict the sale of breads per day. To avoid loss, this company need a system to predict sales prediction easily. With the prediction of the sale, the writer hope that the company can suppress the losses that may occur and optimizing company's profit. This research use Extreme Learning Machine (ELM) method which is method of Artificial Neural Network(ANN) to predict bread sales at Harum Bakery. The Process of prediction using ELM method is started from data normalization, then training process, testing process, find the error value using Mean Square Error (MSE) method to find the smallest error value with some testing, and data denormalization the ELM method is feedforward method with a single hidden layer which is called Single Hidden Layer Feedforward Neural Network (SLFNs). The main purpose of this method is to improve the weakness of other feedforward artificial neural networks, especially in the learning speed. Based on some tests that have been done, the smallest error rate is 0,01616 for white bread using 7 neurons, 4 features, and 5 months of sales data, the best MSE is 0,02839 for sweet bread using 2 neurons, 5 features, and 4 months of sales data, and 0,00812 for cake bread using 7 neurons, 4 features, and 3 months of sales data.