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Modeling the number of unemployed in South Sumatra Province using the exponential smoothing methods

Author

Listed:
  • Rendra Gustriansyah

    (Universitas Indo Global Mandiri)

  • Juhaini Alie

    (Universitas Indo Global Mandiri)

  • Nazori Suhandi

    (Universitas Indo Global Mandiri)

Abstract

The number of open unemployment in South Sumatra Province from year to year is found to be unstable. It can cause serious developmental problems. 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. Therefore, this study discusses the best model to predict the number of unemployed in South Sumatra Province. The methods used to identify the best model are Single Exponential Smoothing (SES), Brown’s Exponential Smoothing (BES), and Holt’s Exponential Smoothing (HES). The Exponential Smoothing methods are compared to obtain forecasting results with a minimal error rate. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics are used to measure the performance of the forecasting model. 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. 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.

Suggested Citation

  • Rendra Gustriansyah & Juhaini Alie & Nazori Suhandi, 2023. "Modeling the number of unemployed in South Sumatra Province using the exponential smoothing methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1725-1737, April.
    • Handle: RePEc:spr:qualqt:v:57:y:2023:i:2:d:10.1007_s11135-022-01445-2
    DOI: 10.1007/s11135-022-01445-2
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    References listed on IDEAS

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    1. Christos Katris, 2020. "Prediction of Unemployment Rates with Time Series and Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 673-706, February.
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    4. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
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