Prediksi Produksi Tanaman Perkebunan Kelapa Sawit di Pulau Sumatera Tahun 2023 dengan Algoritma Bayesian Regulation

Elfin Efendi(1*), Surya Fajri(2), S Safruddin(3), Azwar Anas Manurung(4), Lokot Ridwan(5),

(1) Universitas Asahan, Kisaran, Indonesia
(2) Universitas Asahan, Kisaran, Indonesia
(3) Universitas Asahan, Kisaran, Indonesia
(4) Universitas Asahan, Kisaran, Indonesia
(5) Universitas Asahan, Kisaran, Indonesia
(*) Corresponding Author

Abstract


Palm oil production has an essential role in the regional and national economy. Accurate and reliable predictions are fundamental in planning and decision-making in the oil palm plantation sector. This study aims to predict the production of oil palm plantations on the island of Sumatra in 2023 using the Bayesian Regulation algorithm. This method was chosen because it combines historical data and new information and considers the risk factors that affect palm oil production. Historical data on palm oil production on the island of Sumatra were obtained from the Central Bureau of Statistics and analyzed using the Bayesian Regulation algorithm. The oil palm production prediction model is evaluated using accuracy and prediction error metrics. The research results are expected to provide reliable and accurate predictions for palm oil production on the island of Sumatra in 2023. This research produces a reasonably high accuracy rate of 90% (10% margin of error) and a trim MSE level of 0.00388775674 with a target error of 0.009. The research results predict that palm oil production on the island of Sumatra will decrease compared to previous years (2018-2022). This prediction provides more precise insights to stakeholders such as farmers, producers, government, and industry players in production planning, resource management, budget allocation, and effective decision-making. This research also has the potential to contribute to the development of science, especially the development of more sophisticated and efficient prediction methods, both for palm oil production and other plantation sectors, using the Bayesian Regulation method.


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DOI: https://doi.org/10.30645/kesatria.v4i2.182

DOI (PDF): https://doi.org/10.30645/kesatria.v4i2.182.g181

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