Application of Extreme Learning Machine Algorithm (ELM) in Forecasting Inflation Rate in Indonesia

Yonggi, Yonggi Septa Pramadia
Zamahsary Martha
Syafriandi Syafriandi
Tessy Octavia Mukhti

UNP Journal of Statistics and Data Science
Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science

One indicator to determine the economic stability of a country can be seen from the inflation rate of a country. Inflation is an economic symptom in the form of a general increase in prices or a tendency to increase the prices of goods and services in general and continuously. In an effort to anticipate the impact of inflation in the future, an analysis is needed to find out how the development of the inflation rate is by forecasting. Extreme Learning Machine (ELM) is a feed-forward artificial neural network (ANN) algorithm with one hidden layer called Single Hidden Layer Neural Networks (SLFNs). Based on the research, forecasting the inflation rate in Indonesia using the Extreme Learning Machine algorithm obtained the best architecture  (12,48,1) with a MAPE value of 11%. These results show good forecasting because the resulting MAPE is relatively low.