PERBANDINGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN METODE DOUBLE EXPONENTIAL SMOOTHING DARI HOLT DALAM MERAMALKAN NILAI IMPOR DI INDONESIA

YULINAR I. AJUNU, NOVIANITA ACHMAD, MUHAMMAD REZKY FRIESTA PAYU

Abstract


As a form of purchased goods from other state’s imports have impacts both positive and negative to the states’s condition; therefore, prediction is required. Employing Autoregressive Integrated Moving Average (ARIMA) and Holt’s Double Exponential Smoothing (DES) methods, this study intends to identify which of the methods is the most accurate to predict Indonesia’s import value.  The ARIMA method stage involved: data ploting, data stasioneriation, temporary model identification, parameter estimation, test residual assumption, and prediction. Moreover, the Holt’s DES method involved: data plotting, initial value determination, optimal parameter identification, Level Lt and Trend Tt value quantification, andprediction. The result shows that ARIMA method is the most accurate method to predict Indonesia’s import value.

Keywords


ARIMA, Exponential Smoothing, Import, MAPE, Forecasting.

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References


Armstrong, J. S. (ed.) (2001), Principles of forecasting: a handbook for researchers and practitioners, International series in operations research & management science, Boston, MA: Kluwer Academic.

Chang, P.-C., Wang, Y.-W., and Liu, C.-H. (2007), “The development of a weighted evolving fuzzy neural network for PCB sales forecasting,” Expert Systems with Applications, 32, 86–96. https://doi.org/10.1016/j.eswa.2005.11.021.

Ekananda, M. (2016), Analisis Ekonometrika Dan Analisis Time Series, Jakarta: Mitra Wacana Media.

Enders, W. (2004), Applied econometric time series, Wiley series in probability and mathematical statistics, Hoboken, NJ: J. Wiley.

Hartati, H. (2017), “PENGGUNAAN METODE ARIMA DALAM MERAMAL PERGERAKAN INFLASI,” Jurnal Matematika Sains dan Teknologi, 18, 1–10. https://doi.org/10.33830/jmst.v18i1.163.2017.

Heizer, J., and Render, B. (2014), Operations management: sustainability and supply chain management, Boston: Pearson.

Holt, C. C. (2004), “Forecasting seasonals and trends by exponentially weighted moving averages,” International Journal of Forecasting, 20, 5–10. https://doi.org/10.1016/j.ijforecast.2003.09.015.

Mahmudi, M., Irwandi, R., Rahmadaini, R., and Fadhilah, R. (2018), “Meramalkan Laju Inflasi Menggunakan Metode Pemulusan Eksponensial Ganda,” Journal of Data Analysis, 1, 12–20. https://doi.org/10.24815/jda.v1i1.11863.

Makridakis, S. G., Wheelwright, S. C., and Hyndman, R. J. (1998), Forecasting: methods and applications, New York: John Wiley & Sons.

Sedyaningrum, M., and Nuzula, N. F. (2016), “pengaruh Jumlah Nilai Ekspor, Impor dan Pertumbuhan Ekonomi terhadap Nilai Tukar dan Daya Beli Masyarakat di Indonesia,” Jurnal Administrasi Bisnis, 34.

Sukirno, S. (2012), Makroekonomi: Teori pengantar, Jakarta: Raja Graffindo.

Wahyuningsih, N., Suprapti H., S., and Amutu, S. D. (2017), “Model peramalan Plywood PT. Linggar JatiMahardika Mulia,” Malang: UIN Malang, pp. 52–57.

Wei, W. W. S. (2006), Time series analysis: univariate and multivariate methods, Boston: Pearson Addison Wesley.

Yaffee, R. A., and McGee, M. (2000), Introduction to time series analysis and forecasting: with applications in SAS and SPSS, San Diego: Academic Press.

Zivot, E., and Wang, J. (2006), Modeling financial time series with S-plus, New York, NY: Springer.




DOI: https://doi.org/10.34312/jjps.v1i1.5393

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