Shallot Price Forecasting in Three Locations in Indonesia Using Generalized Space-Time Autoregressive Model

Lidwina Galuh Wandira, Mike Prastuti

Abstract


Shallots are one of the commodities that have an important role for the economy in Indonesia. Many shallot farmers, especially in production center areas, depend for their economy on shallot farming. The price of shallots in Indonesia during 2010-2022 fluctuated quite a bit. This is because the demand for shallots tends to increase over time, while shallot production is seasonal, and the distribution is uneven. The fluctuation of shallot prices and the huge costs of shallot farming result in risk and uncertainty for farmers. The forecasting method used is Generalized Space-Time Autoregressive (GSTAR). The results of the best model for predicting shallot prices in three locations in Indonesia, namely Cirebon, Tegal, and Madiun based on RMSE values, namely the GSTAR (31)-I(1) model use inverse distance normalization weights. Forecasting results for the highest shallot prices in Cirebon, Tegal and Madiun occur in the first week of August 2022. Meanwhile the lowest shallot prices in Cirebon and Madiun occur in the fifth week of August 2022, however the lowest shallot prices in Tegal occur in the fourth week of August 2022. Shallot price movement patterns in Cirebon, Tegal, and Madiun for the next 14 periods will continue to fluctuate but tends to show a downward trend. This was caused by several regions entering the harvest season, resulting in a spike in yields at the same time. As a result, the yield of shallots in the three locations was abundant and caused the price of shallots to decrease. 


Keywords


Forecasting; GSTAR; Shallot Prices

Full Text:

PDF

References


M. O. T. R. O. Indonesia, “Shallot Commodity Profile,” Dec. 2012.

A. M. Windhy, Y. T. Suci, A. S. Jamil, “Analisis Peramalan Harga Bawang Merah Nasional Dengan Pendekatan Model Arima,” PROSIDING Seminar Nasional Fakultas Pertanian Universitas Jambi, 2018.

S. Alma, “Analisis Permodalan Usahatani Tanaman Bawang Merah (Kasus : Desa Cinta Dame, Kecamatan Simanindo, Kabupaten Samosir),” 2021.

S. Setiawan, M. Prastuti, S. Setiawan, and M. Prastuti, “S-GSTAR-SUR model for seasonal spatio temporal data forecasting,” 2016. [Online]. Available: http://einspem.upm.edu.my/journal

B. N. Ruchjana, S. A. Borovkova, H. P. Lopuhaa, E. T. Baskoro, D. Suprijanto, “Least squares estimation of Generalized Space Time AutoRegressive (GSTAR) model and its properties,” In AIP Conference Proceedings-American Institute of Physics, vol. 1450, no. 1, pp. 61, 2012.

U. S. Pasaribu, U. Mukhaiyar, M. N. Heriawan, “Spatial weight determination of GSTAR (1; 1) model by using kernel function,” In Journal of Physics: Conference Series, vol. 1028, no. 1, pp. 012223, 2018.

C. Kasemset, K. Phuruan, T. Opassuwan, “Shallot Price Forecasting Models: Comparison among Various Techniques,” Production Engineering Archives, 29(4), 348-355.

M. Mardianto, N. Afifah, S. A. D. Safitri, I. Syahzaqi, S. Sediono, “Estimated price of shallots commodities national based on parametric and nonparametric approaches,” AIP Conference Proceedings, vol. 2329, no. 1, 2021.

D. W. L. Lestari, S. K. Dini, “Forecasting The Price of Shallots and Red Chilies Using The ARIMAX Model,” EKSAKTA: Journal of Sciences and Data Analysis, pp. 42-49, 2024.

S. Suhartono, D. D. Prastyo, H. Kuswanto, M. H. Lee, “Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR models for forecasting oil production,” Matematika, pp. 103-111, 2018.

S. C. Nelson, G. Bhanage, D. Raychaudhuri, “GSTAR: generalized storage-aware routing for mobilityfirst in the future mobile internet,” Proceedings of the sixth international workshop on MobiArch, pp. 19-24, 2014.

F. J. Simoes, C. T. Yang, “GSTARS computer models and their applications, Part II: Applications,” International Journal of Sediment Research, 23(4), pp. 299-315, 2017.

R. Sigalingging, S. Nababan, N. S. Vinolina, L. A. Harahap, “Modelling of energy productivity prediction systems of shallots classification growth phase system using convolutional neural network,” Procedia Computer Science, no. 216, pp. 328-337, 2021.

D. D. Prastyo, F. S. Nabila, Suhartono, M. H. Lee, N. Suhermi, S. F. Fam, “VAR and GSTAR-based feature selection in support vector regression for multivariate spatio-temporal forecasting,” Soft Computing in Data Science: 4th International Conference, SCDS 2018, Bangkok, Thailand, August 15-16, 2018, Proceedings 4, pp. 46-57, 2019.




DOI: http://dx.doi.org/10.12962/j23378557.v10i1.a17592

Refbacks

  • There are currently no refbacks.


Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License. IPTEK The Journal of Engineering published by Pusat Publikasi Ilmiah, Institut Teknologi Sepuluh Nopember

 

Please contact us for order or further information at: email: iptek.joe[at]gmail.com Fax/Telp: 031 5992945. Editorial Office Address: Pusat Riset Building 6th floor, ITS Campus, Sukolilo, Surabaya 60111, Indonesia.