Application of ARIMAX-LSTM Model in Forecasting the Price of Broiler Chicken in Central Java
DOI:
https://doi.org/10.36456/jstat.vol18.no1.a10555Keywords:
ARIMAX, Broiler Chicken Price, Hybrid, LSTM, Price PredictionAbstract
Central Java's economy grew 4.98% in 2023 with the trade sector as the main driver, including the broiler chicken meat commodity whose production increased from 621,718.06 tons (2021) to 791,997.10 tons (2023). However, the price of this commodity experiences considerable fluctuations, mainly influenced by external factors such as increased demand during the national holiday period and the price of substitute products such as chicken eggs and beef that can affect the purchasing power of broiler chicken meat. Data on chicken meat prices, chicken egg prices, and beef prices were obtained from the official website of PIHPS (Strategic Food Price Information Center), while data for the week before the holiday was obtained using the Python library “holidays”. This research develops a Hybrid ARIMAX-LSTM model to predict chicken meat prices more accurately. The ARIMAX model is used to capture the linear pattern of chicken egg prices by considering external variables (egg prices, beef, and national holidays), while the LSTM captures non-linear residual patterns that cannot be explained by the ARIMAX model. The results show that the Hybrid model produces a MAPE of 1.19%, which is more accurate than the single ARIMAX (MAPE 1.38%). The predicted January 2025 price ranges from IDR 35,300 - IDR 35,900/kg, showing stability without extreme fluctuations. This research provides a predictive solution that can be used by the government and businesses in price control and market stabilization.
References
[1] Badan Pusat Statistik, “Perekonomian Jawa Tengah Tahun 2023 Mengalami Pertumbuhan Sebesar 4,98 persen - Berita,” jateng.bps.go.id. Accessed: Feb. 13, 2025. [Online]. Available: https://jateng.bps.go.id/id/news/2024/02/05/642/perekonomian-jawa-tengah-tahun-2023-mengalami-pertumbuhan-sebesar-4-98-persen.html
[2] Badan Pusat Statistik Indonesia, “Produksi Daging Ayam Ras Pedaging menurut Provinsi - Tabel Statistik - Badan Pusat Statistik Indonesia.” Accessed: Feb. 12, 2025. [Online]. Available: https://www.bps.go.id/id/statistics-table/2/NDg4IzI=/produksi-daging-ayam-ras-pedaging-menurut-provinsi.html
[3] Badan Pusat Statistik Indonesia, “Peternakan Dalam Angka 2023 - Badan Pusat Statistik Indonesia.” Accessed: Feb. 11, 2025. [Online]. Available: https://www.bps.go.id/id/publication/2023/12/22/5927b06e1dcde219f76cec59/peternakan-dalam-angka-2023.html
[4] PIHPS Nasional, “Berdasarkan Daerah - PIHPS.” Accessed: Feb. 13, 2025. [Online]. Available: https://www.bi.go.id/hargapangan/TabelHarga/PasarTradisionalDaerah
[5] T. Trimono, I. G. S. M. Diyasa, K. M. Hindrayani, and M. Idhom, “Model ARIMA-ARCH/GARCH dan Ensemble ARIMA-ARCH/GARCH untuk Prediksi Kerugian pada Harga Komoditas Pertanian,” Prosiding Seminar Nasional Sains Data, vol. 1, no. 1, pp. 1–11, Oktober 2021, doi: https://doi.org/10.33005/senada.v1i01.11.
[6] D. W. Lestari and S. K. Dini, “Forecasting The Price Of Shallots And Red Chilies Using The ARIMAX Model,” EKSAKTA: Journal of Sciences and Data Analysis, vol. 5, no. 1, pp. 42–49, 2024.
[7] L. H. Hasibuan, D. M. Putri, M. Jannah, and S. Musthofa, “Analisis Metode Single Exponential Smoothing Dan Metode Regresi Linear Untuk Prediksi Harga Daging Ayam Ras,” Math Educa Journal, vol. 6, no. 2, Art. no. 2, Oct. 2022, doi: 10.15548/mej.v6i2.3872.
[8] T. M. P. Wiryawanto, Z. Hawani, and M. A. Ramadhani, “Comparison of Support Vector Machine (SVM) and Autoregressive Integrated Moving Average (ARIMA) Methods for Predicting Air Quality Using Python and KNIME,” J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, vol. 16, no. 1, Art. no. 1, Jul. 2023, doi: 10.36456/jstat.vol16.no1.a6633.
[9] R. Arianti, S. Sahriman, and L. P. Talangko, “Model ARIMA dengan Variabel Eksogen dan GARCH pada Data Kurs Rupiah,” ESTIMASI: Journal of Statistics and Its Application, pp. 41–48, 2022.
[10] A. T. Damaliana, K. M. Hindrayani, and T. M. Fahrudin, “Hybrid Holt Winter-Prophet method to forecast the num-ber of foreign tourist arrivals through Bali’s Ngurah Rai Airport,” IJDASEA (International Journal of Data Science, Engineering, and Analytics), vol. 3, no. 2, Art. no. 2, 2023, doi: 10.33005/ijdasea.v3i2.8.
[11] P. N. Yulisa, M. A. Haris, and P. R. Arum, “Peramalan Nilai Ekspor Migas di Indonesia dengan Model Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU),” J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, vol. 16, no. 1, Art. no. 1, Jul. 2023, doi: 10.36456/jstat.vol16.no1.a6121.
[12] P. A. Riyantoko, T. M. Fahrudin, D. A. Prasetya, T. Trimono, and T. D. Timur, “Analisis Sentimen Sederhana Menggunakan Algoritma LSTM dan BERT untuk Klasifikasi Data Spam dan Non-Spam,” Prosiding Seminar Nasional Sains Data, vol. 2, no. 1, Art. no. 1, Dec. 2022, doi: 10.33005/senada.v2i1.53.
[13] M. R. Susila, M. Jamil, and B. H. Santoso, “Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia,” Jambura Journal of Mathematics, vol. 5, no. 1, Art. no. 1, Jan. 2023, doi: 10.34312/jjom.v5i1.14889.
[14] A. S. R. Zega, A. K. Hidayat, N. T. Jannah, and F. Kartiasih, “Selecting The Best Model For Forecasting Indonesia’s Oil And Gas Import Value Using Arimax And Arimax-Lstm,” Dynamic Management Journal, vol. 8, no. 4, pp. 912–941, 2024, doi: http://dx.doi.org/10.31000/dmj.v8i4.
[15] G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, Jan. 2003, doi: 10.1016/S0925-2312(01)00702-0.
[16] K. M. Hindrayani, I. G. S. Mas Diyasa, P. A. Riyantoko, and T. M. Fahrudin, “Studi Literatur Mengenai Prediksi Harga Saham Menggunakan Machine Learning,” santika, vol. 1, pp. 71–75, Nov. 2020, doi: 10.33005/santika.v1i0.20.
[17] A. Muhaimin and T. Trimono, “Stock Price Modeling with Geometric Brownian Motion and Value with Risk PT Ciputra Development TBK,” Nusantara Science and Technology Proceedings, pp. 177–186, May 2023, doi: 10.11594/nstp.2023.3329.
[18] T. Trimono, A. Sonhaji, and U. Mukhaiyar, “Forecasting Farmer Exchange Rate In Central Java Province Using Vector Integrated Moving Average,” Media Statistika, vol. 13, no. 2, pp. 182–193, Dec. 2020, doi: 10.14710/medstat.13.2.182-193.
[19] R. K. Putri and M. Athoillah, “Identifikasi Berita Hoax Terkait Virus Corona Menggunakan Long Short-Term Memory,” in Seminar Nasional Hasil Riset dan Pengabdian, 2022, pp. 506–513.
[20] B. Dissanayake, O. Hemachandra, N. Lakshitha, D. Haputhanthri, and A. Wijayasiri, “A Comparison of ARIMAX, VAR and LSTM on Multivariate Short-Term Traffic Volume Forecasting,” Conference of open innovations association, FRUCT, pp. 564–570.
