Application of Prediction Models Based on Moving Average, Exponential Smoothing and Trend Analysis on Indonesian Palm Oil Exports - Baidawi, Taufik; Effendi, Muhammad Ridwan; Kuswara, Heri; Wardah, Siti; Falgenti, Kursehi (JURNAL TEKNIK KOMPUTER AMIK BSI, 2025)
Palm oil is a strategic commodity for Indonesia, significantly contributing to state revenue and foreign exchange. In 2022, its export value reached USD 33.7 billion. Accurate forecasting of palm oil exports is crucial due to fluctuating market conditions influenced by global demand, prices, and government policies. However, existing studies on forecasting Indonesian palm oil exports are limited, with most research focusing on other agricultural commodities. This study applies Moving Average, Exponential Smoothing, and Trend Analysis methods to forecast palm oil exports and determine the most accurate method. The results show that the Trend Analysis method yields the lowest Mean Absolute Deviation (MAD = 18505.67) and Mean Squared Error (MSE = 436747200), indicating superior accuracy compared to the other methods. The findings suggest that Trend Analysis can provide stakeholders government, companies, and farmers with valuable insights for strategic decision-making. This research contributes to the development of more precise forecasting models, supporting Indonesia's palm oil industry in maintaining its global competitiveness.