Ensemble Boosting Models for Forecasting Rice Prices in Indonesia
DOI:
https://doi.org/10.34123/jurnalasks.v18i1.973Keywords:
Rice Price, Ensemble Boosting, GBM, LightGBM, ForecastingAbstract
Introduction/Main Objectives: Rice is a key staple commodity influencing food security and inflation in Indonesia, making accurate price forecasting essential. In this study, we aim to compare ensemble boosting models and identify the best-performing model for rice price prediction. Background Problems: Notably, rice prices exhibit non-linear patterns over time, while classical statistical methods have limitations in capturing such complexities, resulting in suboptimal forecasting performance. Novelty: This study proposes a lag-based approach that uses lag variables as the only predictors, arranged across multiple input schemes to flexibly capture historical patterns without external variables. Research Methods: Daily national medium rice price data (Jan 2021–Jan 2026) from the National Food Agency are modeled using Gradient Boosting Machine (GBM) and LightGBM, with hyperparameter tuning via Optuna. The forecasting framework relies exclusively on significant lag variables without incorporating exogenous factors. Model performance is evaluated using RMSE, MAE, and MAPE. Findings/Results: LightGBM with optimized hyperparameters achieves the best performance (RMSE = 66.389; MAE = 50.213; MAPE = 0.362%). Furthermore, forecasts for the next 89 days indicate stable prices around Rp13,360–Rp13,395/kg, with no significant fluctuations.
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References
U.S. Department of Agriculture, "Rice - Rice Sector at a Glance," Economic Research Service. Accessed: Aug. 05, 2025. [Online]. Available: https://www.ers.usda.gov/topics/crops/rice/rice-sector-at-a-glance#Global
Badan Pusat Statistik, Ringkasan Eksekutif Pengeluaran dan Konsumsi Penduduk Indonesia Maret 2025. Jakarta, 2025.
A. Fauzi, B. Firmansyah, and D. P. Sari, "Analisis Volatilitas Harga Beras dan Segmentasi Wilayah Berbasis Data Digital Pemanfaatan data crawling dari Panel Harga Pangan Nasional," in Seminar Nasional Official Statistics, 2025, pp. 277-286. doi: 10.34123/semnasoffstat.v2025i1.2420.
A. M. Ginting, "Strategi Kebijakan Mencegah Kenaikan Harga Beras di Indonesia," 2024.
P. Subianto and G. R. Raka, "Visi, Misi dan Program Prabowo - Gibran," 2023.
H. Santoso, L. Hakim, and H. Magdalena, "Sosialisasi Dampak Kenaikan Beras dengan Prediksi Kebutuhan Beras Masyarakat di Pasar Induk Cipinang dengan Kerjasama Badan Pangan Nasional," J. Abdidas, vol. 5, no. 2, pp. 90-96, 2024, doi: 10.31004/abdidas.v5i2.901.
N. A. Muzakir and M. Z. Yahya, "Analisis Perbandingan Model Double Exponential Smoothing dan ARIMA untuk Prediksi Harga Beras di Indonesia," VARIANSI J. Stat. Its Appl. Teach. Res., vol. 7, no. 1, pp. 7-20, 2025, doi: 10.35580/variansiunm349.
V. Arinal and M. Azhari, "Penerapan Regresi Linear untuk Prediksi Harga Beras di Indonesia," J. Sains dan Teknol., vol. 5, no. 1, pp. 341-346, 2023, doi: 10.55338/saintek.v5i1.1417.
E. D. B. Tarigan, M. F. Balqis, T. A. Hutapea, and D. I. Sihombing, "Peramalan Harga Beras di Indonesia Dengan ARIMA," Sepren, vol. 5, no. 02, pp. 117-126, 2024, doi: 10.36655/sepren.v5i02.1508.
D. A. Fajari, M. F. Abyantara, and H. A. Lingga, "Peramalan Rata-Rata Harga Beras Pada Tingkat Perdagangan Besar Atau Grosir Indonesia Dengan Metode Sarima (Seasonal Arima)," J. Agribisnis Terpadu, vol. 14, no. 1, p. 88, 2021, doi: 10.33512/jat.v14i1.11460.
B. Agan, S. Celik, O. I. Damak, and B. Miba'am, Evaluating the machine learning-based models for predicting carbon neutrality in Sub-Saharan African Nations, no. 0123456789. Springer Netherlands, 2025. doi: 10.1007/s10668-025-06289-y.
D. Saputra, D. R. Trinadi, and D. Agustina, "Perbandingan Metode Random Forest, Linier Regression, SVM Untuk Memprediksi Harga Beras Premium," in Prosiding Seminar Nasional Teknologi Informasi dan Bisnis, 2025, pp. 154-160. doi: 10.47701/yqx4ss53.
M. Andriyani, S. Nurwilda, D. Zatusiva Haq, and D. Candra Rini Novitasari, "Prediksi Harga Beras Premium Tahun 2024 Menggunakan Metode Gradient Boosted Trees Regression," J. Teknol. Inf. J. Keilmuan dan Apl. Bid. Tek. Inform., vol. 18, no. 2, pp. 75-84, 2024, doi: 10.47111/JTI.
I. Sabilirrasyad, N. A. Prasetyo, and M. Hermansyad, "Modeling and Predicting Indonesia Rice Prices Using Hyperparameter Optimization XGBoost," in International Conference On Economics, Business and Information Technology, Institut Teknologi dan Sains Mandala, 2024, pp. 459-470. doi: 10.31967/prmandala.v5i0.1226.
R. De Bin and V. G. Stikbakke, "A Boosting First-Hitting-Time Model for Survival Analysis in High-Dimensional Settings," Lifetime Data Anal., vol. 29, no. 2, pp. 420-440, 2023, doi: 10.1007/s10985-022-09553-9.
I. B. Mustapha et al., "Comparative Analysis of Gradient-Boosting Ensembles for Estimation of Compressive Strength of Quaternary Blend Concrete," Int. J. Concr. Struct. Mater., 2024, doi: 10.1186/s40069-023-00653-w.
G. Ke et al., "LightGBM: A Highly Efficient Gradient Boosting Decision Tree," in 31st Conference on Neural Information Processing Systems, California, 2017, pp. 1-9. doi: 10.5555/3294996.3295074.
M. Kopyt, P. Piotrowski, and D. Baczynski, "Short-Term Energy Generation Forecasts at a Wind Farm-A Multi-Variant Comparison of the Effectiveness and Performance of Various Gradient-Boosted Decision Tree Models," Energies, vol. 17, no. 23, 2024, doi: 10.3390/en17236194.
N. Zhang, Q. An, S. Zhang, and H. Ma, "Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm," Mathematics, vol. 13, no. 1, 2025, doi: 10.3390/math13010071.
Badan Pangan Nasional, "Panduan Implementasi Persyaratan Mutu Dan Label Beras," 2023. [Online]. Available: https://badanpangan.go.id/storage/app/media/Panduan_Implementasi_Persyaratan_Mutu_Dan_Label_Beras_compressed.pdf
M. Alam et al., "Improving Rice Grain Quality Through Ecotype Breeding for Enhancing Food and Nutritional Security in Asia-Pacific Region," Rice, vol. 17, no. 1, 2024, doi: 10.1186/s12284-024-00725-9.
H. F. Fiqa, A. R. Dewi, and R. Pandiya, "Perbandingan Metode ARIMA dan Prophet dalam Prediksi Harga Cabai Rawit di Provinsi Jawa Timur," Pros. Semin. Nas. Sains Data, vol. 4, no. 1, pp. 850-862, 2024, doi: 10.33005/senada.v4i1.350.
M. S. Buntara, H. Napitupulu, and N. Gusriani, "Pemrograman Python Untuk Peramalan Data Deret Waktu Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (Sarima)," In Search, vol. 22, no. 2, pp. 354-362, 2023, doi: 10.37278/insearch.v22i2.774.
W. W. S. Wei, Time Series Analysis Univariate and Multivariate Methods. Boston: Pearson-Addison Wesley, 2006.
S. Annisa, Y. Rahkmawati, and H. Hafid, "Peramalan Harga Minyak Mentah Indonesia Menggunakan Algoritma Random Forest," J. Gaussian, vol. 13, no. 2, pp. 472-478, 2025, doi: 10.14710/j.gauss.13.2.472-478.
W. J. Niu, Z. K. Feng, S. S. Li, H. J. Wu, and J. Y. Wang, "Short-Term Electricity Load Time Series Prediction by Machine Learning Model via Feature Selection and Parameter Optimization Using Hybrid Cooperation Search Algorithm," Environ. Res. Lett., vol. 16, no. 5, 2021, doi: 10.1088/1748-9326/abeeb1.
M. M. Hogsgaard, K. G. Larsen, and M. E. Mathiasen, "The Many Faces of Optimal Weak-to-Strong Learning," in 38th Conference on Neural Information Processing Systems, 2024, pp. 51885-51904. doi: 10.5555/3737916.3739560.
G. Airlangga and A. Liu, "A Hybrid Gradient Boosting and Neural Network Model for Predicting Urban Happiness: Integrating Ensemble Learning with Deep Representation for Enhanced Accuracy," Mach. Learn. Knowl. Extr., vol. 7, no. 1, pp. 1-23, 2025, doi: 10.3390/make7010004.
E. Q. Shehab, F. F. Taha, S. H. Muhodir, H. Imran, K. A. Ostrowski, and M. Piechaczek, "Gradient Boosting Regression Tree Optimized with Slime Mould Algorithm to Predict the Higher Heating Value of Municipal Solid Waste," Energies, vol. 17, no. 17, 2024, doi: 10.3390/en17174213.
M. Saied, S. Guirguis, and M. Madbouly, "A Comparative Study of Using Boosting-Based Machine Learning Algorithms for IoT Network Intrusion Detection," Int. J. Comput. Intell. Syst., vol. 16, no. 177, 2023, doi: 10.1007/s44196-023-00355-x.
M. Gan, S. Pan, Y. Chen, C. Cheng, H. Pan, and X. Zhu, "Application of the Machine Learning LightGBM Model to the Prediction of the Water Levels of the Lower Columbia River," J. Mar. Sci. Eng., vol. 9, no. 5, 2021, doi: 10.3390/jmse9050496.
D. Zhao, Z. Hu, Y. Yang, and Q. Chen, "Energy Conservation for Indoor Attractions Based on NRBO-LightGBM," Sustain., vol. 14, no. 19, 2022, doi: 10.3390/su141911997.
J. Zhao, J. He, J. Wan, and K. Liu, "Energy Consumption Prediction for Electric Buses Based on Traction Modeling and LightGBM," World Electr. Veh. J., vol. 16, no. 3, p. 159, 2025, doi: 10.3390/wevj16030159.
A. K. Karakutuk, O. Ozdemir, and S. Senturk, "Optuna-Optimized Pythagorean Fuzzy Deep Neural Network: A Novel Framework for Uncertainty-Aware Image Classification," Appl. Sci., vol. 15, no. 20, p. 11097, 2025, doi: 10.3390/app152011097.
L. Wang, C. Zhang, W. Wang, T. Deng, T. Ma, and P. Shuai, "Slope Stability Assessment Using an Optuna-TPE-Optimized CatBoost Model," Eng, vol. 6, no. 8, p. 185, 2025, doi: 10.3390/eng6080185.
Q. Qin and L. Li, "A VMD-Based Four-Stage Hybrid Forecasting Model with Error Correction for Complex Coal Price Series," Mathematics, vol. 13, no. 18, p. 2912, 2025, doi: 10.3390/math13182912.
M. Alharithi, E. M. Almetwally, O. Alotaibi, M. M. Eid, E. S. M. El-kenawy, and A. A. Elnazer, "A Comparative Study of Statistical and Intelligent Classification Models for Predicting Airlines Passenger Management Satisfaction," Alexandria Eng. J., vol. 119, pp. 99-110, 2025, doi: 10.1016/j.aej.2025.01.109.
X. Wen, M. Jaxa-Rozen, and E. Trutnevyte, "Accuracy Indicators for Evaluating Retrospective Performance of Energy System Models," Appl. Energy, vol. 325, p. 119906, 2022, doi: 10.1016/j.apenergy.2022.119906.
A. Fayyazbakhsh, T. Kienberger, and J. Vopava-wrienz, "Comparative Analysis of Load Profile Forecasting: LSTM, SVR, and Ensemble Approaches for Singular and Cumulative Load Categories," Smart Cities, vol. 8, no. 2, p. 65, 2025, doi: 10.3390/smartcities8020065.
Kementerian Pertanian, "Analisis Kinerja Perdagangan Beras," 2024. [Online]. Available: https://satudata.pertanian.go.id/assets/docs/publikasi/1A_Analisis_Kinerja_Perdagangan_Beras_2024_-_publish.pdf
Institute for Development of Economics and Finance, "Dinamika Pangan dan Energi: Dari Gejolak Harga Beras hingga Perbaikan Tata Kelola Impor BBM," 2025.
