Pemodelan Time Series Harga Lump Karet di Provinsi Jambi Menggunakan ARIMA
DOI:
https://doi.org/10.55331/jutmi.v5i1.85Keywords:
lump karet, ARIMA, peramalan hargaAbstract
Karet merupakan salah satu komoditas perkebunan unggulan di Provinsi Jambi yang memiliki peran penting dalam mendukung perekonomian daerah. Salah satu produk karet yang diperdagangkan adalah lump karet, dimana pergerakan harganya sangat dipengaruhi oleh dinamika pasar dan menunjukkan fluktuasi yang cukup tinggi. Pada periode 2023–2025, harga lump karet mengalami kecenderungan meningkat disertai volatilitas yang semakin besar, sehingga diperlukan suatu metode peramalan yang mampu menangkap pola historis data secara akurat. Penelitian ini bertujuan untuk menganalisis dan meramalkan harga lump karet menggunakan pendekatan time series dengan metode Autoregressive Integrated Moving Average (ARIMA). Data yang digunakan merupakan data harga lump karet Unit Pengolahan dan Pemasaran Bokar (UPPB) dengan Kadar Karet Kering (KKK) 56 – 60 persen selama 149 minggu dari Januari 2023 hingga November 2025. Tahapan analisis meliputi identifikasi pola data, transformasi Box – Cox untuk menstabilkan varians, uji stasioneritas menggunakan Augmented Dickey–Fuller, identifikasi model melalui ACF dan PACF, serta evaluasi model berdasarkan signifikansi parameter dan diagnostik residual. Hasil analisis menunjukkan bahwa model ARIMA(1,1,1) merupakan model terbaik dan digunakan untuk melakukan peramalan harga lump karet selama 16 periode ke depan. Hasil peramalan diharapkan dapat menjadi acuan bagi pelaku usaha dan pengambil kebijakan dalam pengelolaan risiko dan perencanaan sektor perkebunan karet.
References
G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, 1976.
P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, Springer, 1997.
Y. Zhang, S. Wang, and J. Phillips, “Forecasting agricultural commodity prices using ARIMA models,” Journal of Forecasting, vol. 37, no. 5, pp. 555–567, 2018.
T. Suryanto and S. Nugroho, “Peramalan harga kelapa sawit menggunakan model ARIMA,” Jurnal Agribisnis Indonesia, vol. 10, no. 2, pp. 123–134, 2022.
R. Putri, A. Hidayat, and M. Prasetyo, “Analisis peramalan harga kopi berbasis ARIMA,” Jurnal Statistika dan Aplikasinya, vol. 7, no. 1, pp. 45–56, 2021.
G. E. P. Box and D. R. Cox, “An Analysis of Transformations,” 1964. doi: 10.1111/j.2517-6161.1964.tb00553.x.
M. Sivaram, A. Hudaya, and A. Hapzi, “Building a Purchase and Prchase Decision: Analysis of Brand Awareness and Brand Loyalty (Case Study of Private Label Products at Alfamidi Stores In Tangerang City),” Dijemss, vol. 1, no. 2, pp. 235–248, 2019, doi: 10.31933/DIJEMSS.
Y. Xu et al., “Advanced multi-sensor optical remote sensing for urban land use and land cover classification: Outcome of the 2018 ieee grss data fusion contest,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 12, no. 6, pp. 1709–1724, 2019, doi: 10.1109/JSTARS.2019.2911113.
J. N. van Rijn, G. Holmes, B. Pfahringer, and J. Vanschoren, “The online performance estimation framework: heterogeneous ensemble learning for data streams,” Mach. Learn., vol. 107, no. 1, pp. 149–176, 2018, doi: 10.1007/s10994-017-5686-9.
P. Taylor, D. A. Dickey, W. A. Fuller, D. A. Dickey, and W. A. Fuller, “Journal of the American Statistical Association Distribution of the Estimators for Autoregressive Time Series with a Unit Root Distribution of the Estimators for Autoregressive Time Series With a Unit Root,” no. July 2015, pp. 37–41, 2012, doi: 10.1080/01621459.1979.10482531.
A. G. E. P. Box, G. M. Jenkins, S. Journal, R. Statistical, S. Series, and C. A. Statistics, “Some Recent Advances in Forecasting and Control Published by : Wiley for the Royal Statistical Society Some Recent Advances in Forecasting and Controlt,” J. R. Stat. Soc., vol. 17, no. 2, pp. 91–109, 1968.
C. H. Weiß, B. Aleksandrov, M. Faymonville, and C. Jentsch, “Partial Autocorrelation Diagnostics for Count Time Series,” Entropy, vol. 25, no. 1, pp. 1–21, 2023, doi: 10.3390/e25010105.
M. Royer-Carenzi and H. Hassani, “Deviations from Normality in Autocorrelation Functions and Their Implications for MA(q) Modeling,” Stats, vol. 8, no. 1, pp. 1–38, 2025, doi: 10.3390/stats8010019.
L. Seymour, P. J. Brockwell, and R. A. Davis, Introduction to Time Series and Forecasting., vol. 92, no. 440. 1997. doi: 10.2307/2965440.
G. T. Wilson, “Time Series Analysis: Forecasting and Control, 5th Edition, by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel and Greta M. Ljung, 2015. Published by John Wiley and Sons Inc., Hoboken, New Jersey, pp. 712. ISBN: 978‐1‐118‐67502‐1,” J. Time Ser. Anal., vol. 37, no. 5, pp. 709–711, 2016, doi: 10.1111/jtsa.12194.
I. Yanovitzky and A. VanLear, Time Series Analysis Time Series Analysis, no. I. 2018. [Online]. Available: https://us.sagepub.com/sites/default/files/upm-assets/23658_book_item_23658.pdf
P. R. Roberge, Principles and Practice Principles and Practice Editors. 2008. [Online]. Available: https://www.taylorfrancis.com/chapters/edit/10.4324/9781315694108-6/principles-practice-roy-bahl-richard-bird
G. E. P. Box and D. R. Cox, “An Analysis of Transformations,” J. R. Stat. Soc. Ser. B Stat. Methodol., vol. 26, no. 2, pp. 211–243, 1964, doi: 10.1111/j.2517-6161.1964.tb00553.x.
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