Prediksi Harga Kelapa Sawit Menggunakan Metode Extreme Learning Machine
- Jul Hariansyah UIN Sultan Syarif Kasim, Pekanbaru, Indonesia
-
Elvia Budianita
*
UIN Sultan Syarif Kasim, Pekanbaru,
Indonesia
- Jasril Jasril UIN Sultan Syarif Kasim, Pekanbaru, Indonesia
- Iis Afrianty UIN Sultan Syarif Kasim, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Palm oil is one of the keys to the Indonesian economy and the main commodity for attracting foreign investment. The palm oil and palm kernel industry generates most of the foreign currency from palm oil. The price of palm oil often goes up and down every month resulting in instability in the income received by people who own oil palm plantations. The aim of predicting palm oil prices is to carry out appropriate planning or steps for palm oil business actors. One way to overcome this problem is to make predictions. One method that can make predictions is the Extreme Learning Machine (ELM). ELM is an artificial neural network method used to predict palm oil prices. The ELM method is a feedforward method with a single hidden layer which is better known as a single hidden layer feedforward neural network (SLFNs). In this research, the best implementation was 5 inputs with 20 neurons in the hidden layer with output in the form of palm oil price predictions. Based on the tests carried out, the research produced the smallest error rate of 0.0027111424247658633 using 20 neurons in the hidden layer so that the latest data prediction test results for 5 price rotations in September rotation 1 were 1400.314191, September rotation 2 were 1846.798921, September rotation 3 amounted to 1505.430419, September rotation 4 amounted to 2301.853412, September rotation 5 amounted to 2645.082489 in palm oil price predictions.
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