The Prospects of Lampung's Pepper Export to the Global Market: An Analysis Using the ARIMA Model
DOI:
https://doi.org/10.21776/ub.habitat.2023.034.3.26Keywords:
ARIMA, export, forecasting, pepper, time seriesAbstract
Pepper is one of Lampung's leading export commodities. It can be seen from the contribution of Lampung Province's pepper, which accounted for 42 percent of Indonesia's overall pepper exports. However, pepper production and export volume in Lampung Province continue to decline annually. This study aims to analyze the prospect of Lampung’s pepper export to the international market for ten years, from 2023 - 2033. This research used ARIMA (Auto Regressive Moving Average) t model tool using E-views statistical software to forecast the trend of export of Lampung pepper to the International market. The data used was secondary data from the quarterly export of Lampung’s pepper from 2002 to 2022. The study suggested that Lampung's pepper exports are projected to decrease from 2023 to 2033, with a decrease of 10 percent each year. Finally, in 2033, Lampung's pepper exports to the international market only reached 998 tons.
References
Abidin, A.A., Buiney, P.F. and Nohe, D.A. (2022) ‘Peramalan Data Ekspor Kalimantan Barat Dengan Metode Autoregressive Integrated Moving Average ( ARIMA )’, pp. 96–107.
Adebayo, F. A., Sivasamy, R., & Shangodoyin, D.K. (2014). 'Forecasting Stock Market Series with ARIMA Model,' Journal of Statistical and Econometric Methods, 3(3).
Ariesha, Y., Alamsyah, Z. and Malik, A. (2019) ‘Analisis Komparasi Daya Saing Ekspor Lada Indonesia Terhadap Vietnam Dan Malaysia Di Pasar Asean’, Jiseb, 22(1), pp. 80–90. Available at: https://doi.org/10.22437/jiseb.v22i1.8619.
Arsyad, L. (2015) Ekonomi Pembangunan. Yogyakarta: UPP STIM YKPN.
ArunKumar, K.E. et al. (2022). 'Comparative analysis of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) cells, autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA) for forecasting COVID-19 trends', Alexandria Engineering Journal, 61(10), pp. 7585–7603. Available at: https://doi.org/10.1016/j.aej.2022.01.011.
Arzila (2019) 'Indonesian Government Efforts To Teach Vietnam In Exports,' 6, pp. 1–14.
Bandyopadhyay, G. (2016). 'Gold Price Forecasting Using ARIMA Model,' Journal of Advanced Management Science, (March), pp. 117–121. Available at: https://doi.org/10.12720/joams.4.2.117-121.
BPS, (Badan Pusat Statistik) (2021) Ekspor Lada Hitam Menurut Negara Tujuan, 2017-2021.
Brahmana, M.N.E. and Novianti, T. (2022) ‘Daya Saing Dan Faktor Yang Mempengaruhi Ekspor Lada Indonesia Ke Amerika: Pendekatan Revealed Comparative Advantage’, JSEP (Journal of Social and Agricultural Economics), 15(2), p. 113. Available at: https://doi.org/10.19184/jsep.v15i2.28675.
Chandra, D., Ismono , H., & Kasymir, E. (2002) ‘Prospek Perdagangan Kopi Robusta Indonesia di Pasar Internasional’, JIIA Jurnal Ilmu Ilmu Agribisnis, 1(1).
Deka, A., & Resatoglu, N.G. (2019). 'Forecasting foreign exchange rate and consumer price index with arima model: the case of Turkey.', Int. J. Sci. Res. Manag, 7(8), pp. 1254–1275.
Direktorat Jendral Perkebunan (2021) Statistik Perkebunan Unggulan Nasional 2019-2021. Jakarta: Kementerian Pertanian.
Dritsaki, C., Niklis, D. and Stamatiou, P. (2021). 'Oil Consumption Forecasting using ARIMA Models : An Empirical Study for Greece,' 11(4), pp. 214–224.
FAO (2021) Food and Agriculture Organization of the United Nations.
Farooqi, A. (2014). 'ARIMA model building and forecasting on Imports and Exports of Pakistan’, Pakistan Journal of Statistics and Operation Research. [Preprint].
Fattah, J. et al. (2018). 'Forecasting demand using ARIMA model,' International Journal of Engineering Business Management, 10, pp. 1–9. Available at: https://doi.org/10.1177/1847979018808673.
Fitriani, Herdiani, E.T. and Saleh, M. (2002) ‘Pemodelan Autoregressive (AR) pada Data Hilang dan Aplikasinya pada Data Kurs Mata Uang Rupiah’, Jurnal Matematika, Statistika, dan Komputasi, 9(2), pp. 69–85.
Goyal, M. et al. (2021). 'Forecasting of Indian agricultural export using ARIMA model,' Asian Journal of Research in Business Economics and Management, 11(8), pp. 11–15. Available at: https://doi.org/10.5958/2249-7307.2021.00010.4.
Hariadi, W. and Sulantari, S. (2021). 'Application of ARIMA Model for Forecasting Additional Positive Cases of Covid-19 in Jember Regency', Enthusiastic : International Journal of Applied Statistics and Data Science, 1(01), pp. 20–27. Available at: https://doi.org/10.20885/enthusiastic.vol1.iss1.art4.
Haslina, Hasmah, Fitriani, Kadek Widia Asbar, M.A. (2018) ‘Penerapan Metode Arima (Autoregressive Integrated Moving Average) Box Jenkins Untuk Memprediksi Pertambahan Jumlah Penduduk Tansmigran (Jawa Dan Bali) Di Kecamatan Sukamaju, Kabupaten Luwu Utara Propinsi Sulawesi Selatan’, Energies, 6(1), pp. 1–8.
IPC (2017) Pepper Statistical Yearbook 2014. Jakarta: International Pepper Community.
Kemala, S. (2011) Strategi Pengembangan Sistem Agribisnis Lada Untuk Meningkatkan Pendapatan Petani. Bogor: Balai Penelitian Tanaman Obat dan Aromatik.
Kium, N. (2014). Analysis of Malaysian Pepper Market Model. UNIMAS.
Lailiyah, W.H. and Manuharawati, D. (2018) ‘Penerapan Metode Autoregressive Integrated Moving Average (Arima) pada Peramalan Nilai Ekspor Di Indonesia’, Ilmiah Matematika, 6(3), pp. 45–52.
Mahdi, dan S. (2021) ‘Posisi Pasar Lada Indonesia di Pasar Global’, Jurnal Ekonomi Pertanian dan Agribisnis, 5(2), pp. 595–605.
Mahmoud Sayed Agbo, H. (2023). 'Forecasting agricultural price volatility of some export crops in Egypt using ARIMA/GARCH model,' Review of Economics and Political Science, 8(2), pp. 123–133. Available at: https://doi.org/10.1108/REPS-06-2022-0035.
Mejaya, A.S., Fanani, D., & Mawrdi, M.K. (2016) ‘Pengaruh Produksi, Harga Internasional, dan Nilai Tukar terhadap Volume Ekspor (Studi pada Ekspor Global Teh Indonesia Periode tahun 2010-2002)’, Jurnal Administrasi Bisnis (JAB), 35(2).
Nagwani, D.S., and N.K. (2015). 'Application of time series based prediction model to forecast per capita disposable income,' in IEEE International Advance Computing Conference (IACC). Banglore, pp. 454–457.
Nugroho, A.D. and Prasada, I.Y. (2020). 'Performance and forecast of Indonesian pepper exports to Italy,' Bulgarian Journal of Agricultural Science, 26(5), pp. 927–934.
Ospina, R., Gondim, J.A.M. & Leiva, V. (2023). 'An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic : Methodology and Case Study in Brazil', (March 2020), pp. 1–18.
Purnamawati, A. (2002) Dasar-Dasar Ekspor Impor. Yogyakarta: Sinar Grafindo.
Ridha, M., Indra and Safrida (2019) ‘Faktor-faktor yang mempengaruhi ekspor lada Indonesia’, Ilmiah Mahasiswa Pertanian, 4(1), pp. 387–400.
Salwa, N., Tatsara, N., Amalia, R., & Zohra, A.F. (2018) ‘Peramalan harga Bitcoin Menggunakan Metode ARIMA’, Journal of Data Analysis, 1.
Santoso, S. (2009) Panduan Lengkap Menguasai Statistik dengan SPSS. Jakarta: Elex Media Komputindo.
Saputra, Aji Prasetya, Utomo Pujianto, dan Agung Bella, dan A.N. (2022) ‘LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting’, ILKOM Jurnal Ilmiah, 14(1), pp. 57–62.
Siswanto, Eka Satria Wibawa, dan Z.M. (2021) ‘Implementasi Aplikasi Sistem Peramalan Persediaan Barang Menggunakan Metode Single Moving Average Berbasis Web’, Jurnal Ilmiah Elektronika dan Komputer, 14(2), pp. 224–233.
Subagyo (2008) Forecasting Konsep dan Aplikasi. Yogyakarta: BPFE.
Sudjana (2016) Metode Statistika. Bandung: Tarsito.
Sugiyono (2018) Metode Penelitian Kombinasi (Mixed Methods). Bandung: CV Alfabeta.
Wei, W.W. (2006). Time Series Analysis: Univariate and Multivariate Methods. Second Edition. New York: Pearson Education.
Zhou, B., He, D., & Sun, Z. (2006) Traffic Modeling and Prediction using ARIMA/GARCH model, Semantic Scholar.
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