Comparison of Grey-Markov (1,1), Grey-Markov (2,1), and Moving Average Methods in Forecasting Small Sized Data of the Unit Price of Materials in Batam.

Indonesia is one of the developing countries in Southeast Asia. Its population is approximately 257 million. The growing number of people every year has resulted in the increasing of the needs of the community for general development, such as roads, buildings, bridges, and others, in order to assist and launch daily activities. The construction of every bridge or road requires an instrument that can be used to control the fairness of the financial affairs. One of the methods to control financial issues is by forecasting the financial needs for the future. The purpose of this paper is to forecast the unit price of materials in Batam using annual data from 2013 to 2017. Because the available dataset is small, we used three methods that can forecast small sized data: Grey-Markov (1,1), Grey-Markov (2,1), and Moving Average of order 2 (MA(2)). The results of the analysis show that the model which has the smallest error is Grey-Markov (1,1), therefore it is the best model to forecast the unit price of materials in Batam. [ABSTRACT FROM AUTHOR]