Analysis of Trends and Seasonal Patterns of Indonesian Leading Agricultural Commodity Exports: January 2023 – February 2026

Anindhyta Budiarti; Achmad Fakhri Dzulfiqar; Dewi Maryam

doi:10.61255/jeemba.v4i4.1632

Authors

Anindhyta Budiarti Sekolah Tinggi Ilmu Ekonomi Indonesia (STIESIA) Surabaya, Indonesia
Achmad Fakhri Dzulfiqar Sekolah Tinggi Ilmu Ekonomi Indonesia (STIESIA) Surabaya, Indonesia
Dewi Maryam Sekolah Tinggi Ilmu Ekonomi Indonesia (STIESIA) Surabaya, Indonesia

DOI:

https://doi.org/10.61255/jeemba.v4i4.1632

Keywords:

Agricultural Exports, Commodity Trends, Seasonal Patterns, Time Series, Decomposition

Abstract

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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