PERAMALAN HARGA BERAS MENGGUNAKAN METODE HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN NEURAL NETWORK (ARIMA-NN)

Manuela Visakha, Universitas Negeri Yogyakarta
Dhoriva Urwatul Wutsqa, Universitas Negeri Yogyakarta

Abstract


Penelitian ini bertujuan untuk mendeskripsikan model dan ketepatan hasil Hybrid ARIMA-NN dalam meramalkan harga beras di Indonesia tahun 2015-2021 yang bersumber dari Badan Pusat Statistik. Hybrid ARIMA-NN adalah model gabungan model Autoregressive Intregated Moving Average (ARIMA) dan Neural Network. Peramalan dilakukan dengan cara melakukan pemodelan ARIMA terlebih dahulu, kemudian residual dari ARIMA dimodelkan dengan Neural Network. Algoritma dalam Neural Network yang digunakan dalam penelitian ini adalah backpropagation. Hasil peramalan menggunakan Hybrid ARIMA-NN diukur keakuratannya menggunakan Mean Absolute Percentage Error (MAPE). Hasil penelitian ini diperoleh model Hybrid ARIMA(2,0,0) dan NN(2,9,1) yang terdiri dari AR dengan ordo 2, differencing dengan ordo 0, dan MA dengan ordo 0 kemudian model Neural Network dengan 2 neuron input, 9 neuron tersembunyi, dan 1 neuron output. Hasil akurasi model Hybrid ARIMA-NN diperoleh nilai MAPE sebesar 0,9778% yang menunjukkan bahwa model memiliki tingkat keakuratan yang baik.

Kata kunci: peramalan, harga beras, Hybrid ARIMA-NN, Autoregressive Intregated Moving Average, Neural Network.


Full Text:

PDF

References


Apichottanakul, A., Piewthongngam, K., & Pathumnakul, S. (2009). Using an artificial neural network to forecast the market share of Thai rice. 2009 IEEE International Conference on Industrial Engineering and Engineering Management, 665–668. https://doi.org/10.1109/IEEM.2009.5373247

Babu, C. N., & Reddy, B. E. (2014). A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Applied Soft Computing, 23, 27–38. https://doi.org/https://doi.org/10.1016/j.asoc.2014.05.028

Cadenas, E., & Rivera, W. (2010). Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renewable Energy, 35(12), 2732–2738. https://doi.org/https://doi.org/10.1016/j.renene.2010.04.022

Co, H. C., & Boosarawongse, R. (2007). Forecasting Thailand’s rice export: Statistical techniques vs. artificial neural networks. Computers & Industrial Engineering, 53(4), 610–627. https://doi.org/https://doi.org/10.1016/j.cie.2007.06.005

de Araújo Morais, L. R., & da Silva Gomes, G. S. (2022). Forecasting daily Covid-19 cases in the world with a hybrid ARIMA and neural network model. Applied Soft Computing, 126, 109315. https://doi.org/https://doi.org/10.1016/j.asoc.2022.109315

Díaz-Robles, L. A., Ortega, J. C., Fu, J. S., Reed, G. D., Chow, J. C., Watson, J. G., & Moncada-Herrera, J. A. (2008). A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile. Atmospheric Environment, 42(35), 8331–8340. https://doi.org/https://doi.org/10.1016/j.atmosenv.2008.07.020

Gujarati, D. N. (2003). Basic Econometrics. McGraw Hill.

Makridakis, S., Wheelwright, S., & Hyndman, R. J. (1997). Forecasting: Methods and Applications, 3rd Ed.

Ngestisari, W., Susanto, B., & Mahatma, T. (2020). Perbandingan Metode ARIMA dan Jaringan Syaraf Tiruan untuk Peramalan Harga Beras. Indonesian Journal of Data and Science (IJODAS), 1(3), 96–107.

Ohyver, M., & Pudjihastuti, H. (2018). Arima Model for Forecasting the Price of Medium Quality Rice to Anticipate Price Fluctuations. Procedia Computer Science, 135, 707–711. https://doi.org/10.1016/j.procs.2018.08.215

Ömer Faruk, D. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence, 23(4), 586–594. https://doi.org/https://doi.org/10.1016/j.engappai.2009.09.015

Terui, N., & van Dijk, H. K. (2002). Combined forecasts from linear and nonlinear time series models. International Journal of Forecasting, 18(3), 421–438. https://doi.org/https://doi.org/10.1016/S0169-2070(01)00120-0

Wei, W. (2006). Time Series Analysis: Univariate and Multivariate Methods, 2nd edition, 2006.


Refbacks

  • There are currently no refbacks.


 

Online ISSN (e-ISSN): 3031-1152

Creative Commons LicenseJurnal Kajian dan Terapan Matematika by https://journal.student.uny.ac.id/index.php/jktm/index is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.