![]() The increasing number of unemployed is a serious development problem. ![]() ![]() In other words, unemployment is a group of workers who want to work but have not had the opportunity to work. Thus, this SES model can be a reference for the Government to predict the number of open unemployment in South Sumatra Province so that the Regional Government can anticipate the negative impacts it can cause.Īccording to the International Labor Organization (ILO), unemployment is a person belonging to the working-age population group who has not worked for a certain period and is willing to accept work and is looking for work (Resolution 2013). Empirical results show that the SES model with the smoothing parameter value = 0.7 is the best significant model in predicting the number of open unemployment in South Sumatra Province with a MAPE value of 6.24% and an RMSE value of 23.058. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics are used to measure the performance of the forecasting model. The Exponential Smoothing methods are compared to obtain forecasting results with a minimal error rate. The methods used to identify the best model are Single Exponential Smoothing (SES), Brown’s Exponential Smoothing (BES), and Holt’s Exponential Smoothing (HES). Therefore, this study discusses the best model to predict the number of unemployed in South Sumatra Province. One solution to this problem is to build an early warning system by predicting the number of open unemployment in the future so that the Regional Government can establish relative policies to anticipate the negative impacts it will have on the environment, economy, social and politics. It can cause serious developmental problems. The number of open unemployment in South Sumatra Province from year to year is found to be unstable.
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