Prediksi Jumlah Produksi Kelapa Sawit Dengan Menggunakan Metode Extreme Learning Machine (ELM) (Studi kasus: PT. Sandabi Indah Lestari Kota Bengkulu) - Ema Agasta; Imam Cholissodin; Dian Eka Ratnawati (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Palm oil is a plantation that became the number one sector in Indonesia. This plant has a cost and a better production than other plantation crops such as sugar cane and rubber. In a company, palm oil production becomes the driving force of the economy, as well as what happened to PT. Sandabi Indah Lestari. In every week the company plans to predict the production. Planning done sometimes still give less than optimal results. This is because the calculation process is still using manual analysis. In this research will use four prediction features that are plant age, number of trees, land, and production. The prediction technique used is the learning method of Extreme Learning Machine (ELM). This method has advantages in learning speed and accuracy in predicted results. The calculation process starts from the process of data normalization, training a number of training data and test data, calculation of the prediction error value and produce the final value. The data used is production data in the period 2015 - 2017 with a total of 297 data. From a number of data will be divided into two data with percentage of 80% training data and 20% test data. The result of the research was obtained the optimal parameter value that is 13 hidden neuron in testing the number of neurons with Mean Absolute Perscentage (MAPE) value of 21.25%, 20.42% on the data feature test with the best 2 technical features and 20,19% on testing the pattern with the final result of the data pattern 1.