Prediction of Poor Inhabitant Number Using Least Square and Moving Average Method

Abstract

Abstract-The number of poor inhabitant in South Kalimantan decreased within the last three years compared with the previous years. The numbers of poor inhabitant differs from time to time. This scaled dynamical number has been a problem for the local government to take proper polices to solve this matter. It will then be necessary to predict a potential number of poor inhabitants in the next year as the basis on subsequent policy making. This research will apply both Least Square and Moving Average method as the measurement to count prediction values. From the results of the study, the prediction analysis by using those two methods is valid for predicting acquired number of poor inhabitant for the next period according to the data from the previous year. Based on the study, the validity of Least Square was 98.35% and Moving Average was 98.79% by using the data in the last seven years.

Foreign Tourist Arrivals Forecasting Using Recurrent Neural Network Backpropagation through Time

“…The number of observations used is called the order of the series [13]. Previous researches showed that Single Moving Average can be used to forecast time series data [14][15]. In this research Single Moving Average with order 2 or SMA( 2) is used and calculated with formula below.…”