Prediksi Harga Bahan Pokok Nasional Jangka Pendek Menggunakan ARIMA
Downloads
Abstrak” Fluktuasi harga bahan pokok yang tidak terkendali dapat menyebabkan kerugian bagi konsumen maupun produsen. Salah satu langkah untuk mengatasi permasalahan tersebut yaitu dengan membuat prediksi harga yang akurat sehingga tindakan preventif dapat dilakukan untuk meminimalkan gejolak harga. Dalam studi ini, ARIMA digunakan untuk memprediksi harga bahan pokok nasional dalam jangka pendek. Data harga harian dari dua belas bahan pokok pada empat horizon prediksi (1 hingga 30 hari ke depan) digunakan untuk menguji kinerja ARIMA dalam memprediksi harga bahan pokok. Hasil eksperimen menujukkan bahwa model ARIMA yang dihasilkan mampu memprediksi harga dengan tingkat error rata-rata sebesar 2.22%.
Kata Kunci” ARIMA, Bahan Pokok, Prediksi, Peramalan
Abstract” Uncontrolled price fluctuation of basic commodities can harm both consumers and producers. One way to overcome the problem is by making accurate price prediction so that preventive actions can be conducted to minimize the price fluctuation. In this study, ARIMA is used to make short-term price prediction of national basic commodities. Daily pricing data of twelve commodities in four prediction horizons (1 to 30 days ahead) is used to test the performance of ARIMA in predicting the commodity prices. The experimental results showed that the ARIMA model was able to predict the price quite accurately with an average error rate of 2.22%.
Keywords” ARIMA, Basic Commodities, Forecast, Prediction
Bradie, B. (2006). A friendly Introduction to Numerical Analysis. Pearson Prentice Hall.
Ditakristy, M. L., Saepuddin, D., & Nhita, F. (2016). Analisis dan Implementasi Radial Basis Function Neural Network dalam Prediksi Harga Komoditas Pertanian. E-Proceeding of Engineering , 3 (1), 1130–1139.
Ediger, V. Åž., & Akar, S. (2007). ARIMA Forecasting of Primary Energy Demand by Fuel in Turkey. Energy Policy , 35 (3), 1701–1708.
Firdaus, M. (2012). Manajemen Agribisnis. Jakarta: Bumi Aksara.
Goyal, P., Chan, A. T., & Jaiswal, N. (2006). Statistical Models for the Prediction of Respirable Suspended Particulate Matter in Urban Cities. Atmospheric Environment , 40 (11), 2068–2077.
Hikichi, S. E., Salgado, E. G., & & Beijo, L. A. (2017). Forecasting Number of ISO 14001 Certifications in the Americas using ARIMA Models. Journal of Cleaner Production , 147, 242–253.
Hyndman, R. J., & Khandakar, Y. (2008). Automatic Time Series Forecasting: The Forecast Package for R. Journal of Statistical Software , 27 (3), 1–22.
Kementerian Perdagangan Republik Indonesia. (2017). Tabel Harga Kebutuhan Pokok Nasional. Dipetik August 23, 2017, dari Kementerian Perdagangan Republik Indonesia: http://www.kemendag.go.id/id/economic-profile/prices/national-price-table
Liu, P.-W. G. (2009). Simulation of the Daily Average PM10 Concentrations at Ta-Liao with Box–Jenkins Time Series Models and Multivariate Analysis. Atmospheric Environment , 43 (13), 2104–2113.
Mohamed, Z., & Bodger, P. (2005). Forecasting Electricity Consumption in New Zealand using Economic and Demographic Variables. Energy , 30 (10), 1833–1843.
Nanggala, S., Saepuddin, D., & Nhita, F. (2016). Analisis dan Implementasi Elman Recurrent Neural Network untuk Prediksi Harga Komoditas Pertanian. E-Proceeding of Engineering , 3 (1), 1253–1261.
Sen, P., Roy, M., & Pal, P. (2016). Application of ARIMA for Forecasting Energy Consumption and GHG Emission: A Case Study of an Indian Pig Iron Manufacturing Organization. Energy , 116, 1031–1038.
Setiawan, A. F., & Hadianto, A. (2015). Fluktuasi Harga Komoditas Pangan dan Dampaknya Terhadap Inflasi di Provinsi Banten. Skripsi, Institut Pertanian Bogor, Ekonomi.
Widyadarma, D. M., Saepuddin, D., & Nhita, F. (2016). Prediksi Harga Komoditi Pertanian Menggunakan Algoritma Hybrid Jaringan Syaraf Tiruan Arsitektur Elman Dengan Algoritma Genetika. E-Proceeding of Engineering , 3 (1), 1263–1266.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
