Implementasi Metode Backpropagation Untuk Klasifikasi Kenaikan Harga Minyak Kelapa Sawit - Dwi Rahayu; Randy Cahya Wihandika; Rizal Setya Perdana (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Palm oil is a plantation product that is the main export commodity of Indonesia. The increasing amount of processed materials that can be made by using palm oil makes the rise of oil palm demand. The main factor causing an increase in demand for palm oil is a relatively low price compared to its competitor prices such as soybean oil, sunflower seed oil, peanut oil, cotton oil and rapeseed oil. Price becomes an important factor to determine the selling point of the product. Prices also affect the producer's profit. The classification of the possibility of rising or falling prices of palm oil becomes a major consideration of a consumer to buy. This writing discusses the classification of palm oil prices using Backpropagation method. The Backpropagation method will model the coconut oil price data 5 months earlier to find the classification results in the 6th month. Classification results obtained have an accuracy of 69.57% with the number of hidden neurons as much as 50, the value of learning rate as big as 0.1 and the number of maximum iterations of 70,000.