Peramalan Curah Hujan Menggunakan Metode Extreme Learning Machine - Rich Juniadi Domitri Simamora; Tibyani Tibyani; Sutrisno Sutrisno (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Rainfall is the height of rain water that is found and collected in a flat, not absorbed, does not evaporate and does not flow. Information about rainfall is very important especially in agriculture and civil. In agriculture, rainfall information is used to determine the type of plants to be planted in accordance with the intensity of rainfall, predicting the start of the growing season in the planting calendar to minimize the risk of planting. In the civil field, it is used as a determinant of engineering design standards in planning flood disaster control buildings. Above normal rainfall will cause natural disasters such as floods and landslides. Rainfall is part of the weather element and one of the meteorological processes that is quite difficult to predict. Rainfall forecasting is needed so that the community and the government can take preventative measures against the existing problems. The forecasting process is divided into several processes which include data normalization, forecasting with the Extreme Learning Machine algorithm, data denormalization and the results of errors with MAPE. Based on the test results using rainfall data in the Poncokusumo area with a span of years 2002 to 2015 obtained the smallest MAPE value of 3.6852%, with as many features as 4, many neurons in the hidden layer as much as 2, the percentage of training data 90%.