Prediksi Indeks Harga Konsumen (IHK) Kelompok Perumahan, Air, Listrik, Gas, dan Bahan Bakar Menggunakan Metode Extreme Learning Machine

Irma Ramadanti Fitriyani (Fakultas Ilmu Komputer, Universitas Brawijaya)
Budi Darma Setiawan (Fakultas Ilmu Komputer, Universitas Brawijaya)
Rizal Setya Perdana (Fakultas Ilmu Komputer, Universitas Brawijaya)

Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Vol 2 No 11 (2018): November 2018

The Consumer Price Index is one of the indicators to measure the inflation rate in Indonesia. In 2017 inflation in Indonesia by expenditure group in general is 3,61%. The group of housing, water, electricity, gas, and fuel become the biggest contributor of inflation compared to six other groups with 5,14%. Therefore, the prediction needs to be done to anticipate and reduce domestic inflation rate. Prediction done in this research using method of Extreme Learning Machine (ELM) with initialization of weight using Nguyen-Widrow initialization. The data used in this research are 84 Consumer Price Index data of housing, water, electricity, gas, and fuel from January 2011 until December 2017. The data obtained from the official website of Bank Indonesia (www.bi.go.id). The result of this research is the minimum RMSE value of 0,72 with the number of features = 7, the amount of training data is 30 and the testing data is 11, the number of hidden neurons = 7, and the activation function is sigmoid binary function.