<![CDATA[Purpose – This study identifies monthly export trends of 13 leading Indonesia agricultural commodities (January 2023-February 2026), Analyze seasonal patterns via seasonal indices, compare export performance using CAGR and Coefficient of Variation (CV), and develop commodity segmentation for risk management and export planning. Design/methodology/approach – A descriptive-quantitative approach combined time series decomposition (additive and multiplicative), 3- and 6 month moving-average, CAGR, CV, and K-Means clustering on secondary monthly export data (USD million) for 13 commodities over 38 months from Statistics Indonesia (BPS) and analyzed using Python on Google Colaboratory. Finding/Results – Decomposition revealed seasonal patterns, with Coffee showing strongest amplitude, peaking in October and troughing in April. CAGR ranked Coffee (65,21%), Black Pepper, and Perennial Fruits as fastest-growing, while Maize, Seaweed, and Bird’s Nest contracted most. Bird’s Nest and Medicinal Plants were most stable, Maize and Black Pepper were most volatile. K-Means clustering (k=2) isolated Coffee as a distinct high-value Cluster (mean export USD 138,75 million). Informing a four-quadrant strategic Matrix combining CAGR, CV, and mean export value. Originality/Value – This study addresses five research gaps through multi-commodity coverage, monthly temporal granularity, integrated growth-volatility metrics, an evidence-based typology via K-Means clustering, and an interactive Python dashboard for real-time monitoring. Offering an empirical basis for differentiated export risk management strategies.]]>
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