Analisis Prediksi Produksi Jagung Menggunakan Algoritma Triple Exponential Smoothing Untuk Mendukung Strategi Ketahanan Pangan Nasional
Abstract
Indonesia, as an agrarian country, faces persistent challenges in stabilizing corn production due to climate variability, fertilizer distribution, pest attacks, and agricultural policies. These fluctuations directly affect food supply and livestock feed, highlighting the need for adaptive, data-driven management strategies. This study aims to forecast national corn production trends by utilizing monthly data from the Central Bureau of Statistics (BPS) covering January 2020 to September 2025, with a total of 69 observations. The research employs a Triple Exponential Smoothing (Holt-Winters) approach within a descriptive-analytical quantitative framework. The research stages include data preprocessing, model development, and evaluation using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics.
The results demonstrate that the Holt-Winters model effectively captures both trend and seasonal patterns in corn production, achieving an RMSE of 264,199 tons and a MAPE of 14.97%, which indicates a moderate and acceptable level of forecasting accuracy for agricultural time series analysis. Furthermore, the integration of the model into a Streamlit-based application enables interactive visualization, allowing non-technical users to upload data and access real-time forecasting results. This study confirms the effectiveness of Holt-Winters in forecasting agricultural production and provides practical contributions to national food security planning, offering a sustainable and accessible predictive solution for policymakers, industry stakeholders, and researchers.
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