Forecasting Rice Production Volume in Pariaman City Using the Autoregressive Integrated Moving Average Approach
DOI:
https://doi.org/10.31102/zeta.2025.10.2.123-129Keywords:
Rice Production, ARIMA, Pariaman City, ForecastingAbstract
In Indonesia, rice plays an important role as the main food source, considering that more than most of the population relies on rice as the main food source, and the demand for rice continues to increase every year along with the increasing population. Statistic Indonesia (Badan Pusat Statistik) reports that the consumption of rice in 2023 in Pariaman City was around 9,361.46 tons. This makes the agricultural sector, especially rice, one of the main pillars of food security in Pariaman City. This research aims to forecast the amount of rice yield in Pariaman City by applying the ARIMA method. By using ARIMA, patterns and variations in the number of rice yields in Pariaman City can be known more precisely so as to produce an accurate picture for the decision-making process. The ARIMA (0,2,1) model was chosen as the superior model based on the lowest mean square value obtained which is 0.466956, which indicates that the model is quite effective in predicting rice production. Based on the pattern of prediction results, rice paddy production for the next five periods is expected to experience a small spike from period to period, but the increase is not too significant.
Downloads
References
R., Zamahsary Martha, Dony Permana, & Fadhilah Fitri. (2024). K-Medoids Cluster Analysis for Grouping Provinces in Indonesia Based on Agricultural Households ST2023. UNP Journal of Statistics and Data Science, 2(3), 324–329. https://doi.org/10.24036/ujsds/vol2-iss3/193
BPS Pariaman. (2024). Luas Panen dan Produksi Kota Pariaman berdasarkan Metode Kerangka Sampel Area (KSA) 2022–2023 (BPS Pariaman (ed.)). BPS Kota Pariaman. https://s.id/ksapariaman
Fitriani, Tri Herdiani, E., & M. Saleh AF. (2013). Pemodelan Autoregressive (AR) pada Data Hilang dan Aplikasinya pada Data Kurs Mata Uang Rupiah. Jurnal Matematika, Statistika, Dan Komputasi, 9(2), 69–85.
Martadona, I. (2021). Analisis Ketahanan Pangan Rumah Tangga Petani Padi Berdasarkan Proporsi Pengeluaran Pangan di Kota Padang. 167–174.
Qamara, L. N., Wahyuningsih, S., Deny, F., & Amijaya, T. (2019). Peramalan Harga Minyak Mentah Menggunakan Model Autoregressive Integrated Moving Average Neural Network (ARIMA-NN) Forecasting Crude Oil Prices Using Autoregressive Integrated Moving Average Neural Network (ARIMA-NN) Model. Jurnal EKSPONENSIAL, 10(2), 127–134.
Setiawan, R. N. S., & Kusuma, W. (2024). Peramalan Jumlah Produksi Padi Di Nusa Tenggara Barat Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (Sarima). Jurnal Agrimansion, 25(1), 106–114. https://doi.org/10.29303/agrimansion.v25i1.1624
Suryana, A., & Kariyasa, K. (2016). Ekonomi Padi di Asia: Suatu Tinjauan Berbasis Kajian Komparatif. Forum Penelitian Agro Ekonomi, 26(1), 17. https://doi.org/10.21082/fae.v26n1.2008.17-31
Yuliawanti, F. D., Novitasari, D. C. R., Widodo, N., Hamid, A., & Utami, W. D. (2021). Penerapan Metode Autoregressive Integrated Moving Average (Arima) Untuk Prediksi Bilangan Sunspot. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 15(3), 555–564. https://doi.org/10.30598/barekengvol15iss3pp555-564
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Olga Afrilly Putri, Zamahsary Martha, Ikhsan Gunawan P

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.



