Forecasting Total Population Using Chen, Cheng, and Markov Chain Fuzzy Time Series Models

Abstract

Indonesia is a country with a very large population and a fairly high population growth. Population growth is one of the important indicators in demography that will affect the availability of food, land and employment. Hence, population growth data is very important to government when designing development policies. The fuzzy time series models have been widely used in previous studies to forecast enrollment data, stock exchanges and others. In this study, three types of fuzzy time series models were used namely Chen, Cheng, and Markov Chain models to predict the total population in Tanjugpinang city, Riau Islands Province. The models are compared based on error analysis, namely: Mean Average Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The results showed that Markov chain model yielded a very low MAPE, namely 0.0457%. The MAPE value for the Cheng model is 0.0535%, while the MAPE value for the Chen model as the reference model is 0.1428%. Based on the RMSE calculation, the Markov chain gives the best results with a value of 145, RMSE for Cheng 149 and the Chen model gives an RMSE value of 345. The best model obtained for population forecasting is the Markov chain as it has the smallest RMSE, and also the Markov chain model is the most accurate model in the used dataset based on the MAPE value.