Oil Palm Price Prediction Using Holt-Winters Exponential Smoothing at PT Ivo Mas Tunggal

Christoper Jodi Aman Sinaga; Ari Purno Wahyu Wibowo

doi:10.47709/brilliance.v6i2.8704

Oil Palm Price Prediction Using Holt-Winters Exponential Smoothing at PT Ivo Mas Tunggal

Authors

Christoper Jodi Aman Sinaga Universitas Widyatama, Indonesia
Ari Purno Wahyu Wibowo Universitas Widyatama, Indonesia

DOI:

https://doi.org/10.47709/brilliance.v6i2.8704

Keywords:

additive model, forecasting, Fresh Fruit Bunch (FFB) price, Holt-Winters Exponential Smoothing, Mean Absolute Percentage Error, time series

Abstract

Fluctuations in oil palm Fresh Fruit Bunches (FFB) prices over time have created challenges in planning and decision-making processes within plantation companies, including at Nenggala Plantation of PT Ivo Mas Tunggal. Price instability affects production planning, marketing strategies, and risk management, making accurate forecasting essential for supporting managerial decisions-maing. Therefore, this study aims to predict oil palm Fresh Fruit Bunches (FFB) prices using the Holt-Winters Exponential Smoothing method and compare the performance of additive and multiplicative models. This study applies a time series forecasting using historical oil palm FFB price data from January 2020 to September 2024. The research process includes data preprocessing, splitting the dataset into training and testing sets, and forecasting using additive and multiplicative Holt-Winters models. Model performance was evaluated using Mean Absolute Percentage Error (MAPE). The results showed that the additive Holt-Winters model achieved better forecasting accuracy than the multiplicative model, with a MAPE value of 10.08% compared to 11.98%. The forecasting results also indicated that the additive model was able to follow the historical trend and seasonal patterns of oil palm FFB prices effectively. The Holt-Winters Exponential Smoothing method with an additive approach is effective for predicting palm oil prices and can support production planning and managerial decision-making at the plantation level. Future studies are recommended to incorporate external variables and compare the Holt-Winters method with other forecasting approach to improve prediction performance.

References

Ainiyah, L., & Bansori, M. (2021). Prediksi Jumlah Kasus COVID-19 Menggunakan Metode Autoregressive Integrated Moving Average. Jurnal Sains Dasar, 10(2), 62–68.

Al-Khowarizmi, Syah, R., Nasution, M. K. M., & Elveny, M. (2021). Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor. International Journal of Electrical and Computer Engineering, 11(3), 2696–2703. https://doi.org/10.11591/ijece.v11i3.pp2696-2703

Andi, T., Pranolo, A., Ismail, A. R., Juni, C., Kusuma, C., & Dahlan, U. A. (2026). CPSO-LSTM?: Chaotic Particle Swarm Optimization improved LSTM Hyperparameters for Air Pollution Prediction. 11(1), 126–141. https://doi.org/10.15575/join.v11i1.1689

Ardiyanto, A., Tarigan, S. D., Widyastuti, R., Nugroho, B., & Rivai, F. A. (2026). Optimization of Empty Fruit Bunch Application to Improve Soil Organic Carbon , Total Soil Nitrogen , Earthworm Populations , and Oil Palm Performance on Spodosols. 41(2), 199–215.

Devanathan, B., Varshitha, K. J., Kumar, L. P., Lakshmanan, S. A., & Prakash, N. K. (2025). Explainable AI Framework Using XGBoost With SHAP and LIME for Multi-Scale Household Energy Forecasting. IEEE Access, 13, 149750–149764. https://doi.org/10.1109/ACCESS.2025.3602673

Dewi, S. P., Nurwati, N., & Rahayu, E. (2022). Penerapan Data Mining Untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode K-Nearest Neighbor. Building of Informatics, Technology and Science (BITS), 3(4), 639–648. https://doi.org/10.47065/bits.v3i4.1408

I Putu Susila Handika, & I Kadek Susila Satwika. (2023). Enhancing Sales Forecasting Accuracy Through Optimized Holt-Winters Exponential Smoothing with Modified Improved Particle Swarm Optimization. Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 12(2), 203–212. https://doi.org/10.23887/janapati.v12i2.65462

Jayanth, T., & Manimaran, A. (2024). Developing a Novel Hybrid Model Double Exponential Smoothing and Dual Attention Encoder-Decoder Based Bi-Directional Gated Recurrent Unit Enhanced with Bayesian Optimization to Forecast Stock Price. IEEE Access, 12(August), 114760–114785. https://doi.org/10.1109/ACCESS.2024.3435683

Peng, Z. J., Zhang, C., & Tian, Y. X. (2023). Crude Oil Price Time Series Forecasting: A Novel Approach Based on Variational Mode Decomposition, Time-Series Imaging, and Deep Learning. IEEE Access, 11(August), 82216–82231. https://doi.org/10.1109/ACCESS.2023.3301576

Rahmadeni, R. and Egianta, D. (2021). Peramalan Produksi Kelapa Sawit dengan Metode Exponential Smoothing.

Roza, R., Fauzan, M. N., & Rahayu, W. I. (2020). Tutorial Sistem Informasi Prediksi Jumlah Pelanggan Menggunakan Metode Regresi Linier Berganda Berbasis Web Menggunakan Framework Codeigniter. Kreatif. Retrieved from https://books.google.co.id/books?id=ixH9DwAAQBAJ

Sabri, M. S., Khalid, N., Azam, A. H. M., & Sarmidi, T. (2022). Impact Analysis of the External Shocks on the Prices of Malaysian Crude Palm Oil: Evidence from a Structural Vector Autoregressive Model. Mathematics, 10(23). https://doi.org/10.3390/math10234599

Safi, S. K., & Sanusi, O. I. (2021). A hybrid of artificial fneural network, exponential smoothing, and ARIMA models for COVID-19 time series forecasting. Model Assisted Statistics and Applications, 16(1), 25–35. https://doi.org/10.3233/MAS-210512

Taksana, R., Janjamraj, N., Romphochai, S., Bhumkittipich, K., & Mithulananthan, N. (2024). Design of Power Transformer Fault Detection of SCADA Alarm Using Fault Tree Analysis, Smooth Holtz–Winters, and L-BFGS for Smart Utility Control Centers. IEEE Access, 12, 116302–116324. https://doi.org/10.1109/ACCESS.2024.3446804

Trull, O., García-Díaz, J. C., & Troncoso, A. (2020). Initialization methods for multiple seasonal holt-winters forecasting models. Mathematics, 8(2), 1–16. https://doi.org/10.3390/math8020268

Woro Isti Rahayu, A. T. R. A. (2020). Regresi linier untuk prediksi jumlah penjualan terhadap jumlah permintaan. Kreatif. Retrieved from https://books.google.co.id/books?id=VtD9DwAAQBAJ

Yendra, R., Rahman, A., Riyan, A., Putri, A. A., Fakhrunnisa, A., Primasta, B. Z., & Ananda, D. D. (2025). Forecasting Analysis of Palm Oil FFB Price with Holt-Winters Approach: Case Study in Riau Province. International Journal of Mathematics And Computer Research, 13(07), 5373–5376. https://doi.org/10.47191/ijmcr/v13i7.04

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Published

2026-06-26

How to Cite

Sinaga, C. J. A., & Wibowo, A. P. W. (2026). Oil Palm Price Prediction Using Holt-Winters Exponential Smoothing at PT Ivo Mas Tunggal . Brilliance: Research of Artificial Intelligence, 6(2), 275–282. https://doi.org/10.47709/brilliance.v6i2.8704