Analysis of corn production in Indonesia using business intelligence technology based on Power BI

https://doi.org/10.31940/matrix.v15i1.21-31

Authors

  • Muh Kevin Adesyahputra Information Systems, Universitas Semarang, Indonesia
  • Eka Putri Rachmawati Information Systems, Universitas Semarang, Indonesia

Keywords:

business intelligence, corn production, data analytics, ETL, Power BI

Abstract

Corn production trends in Indonesia from 2020 to 2024 were analyzed to address regional disparities and enhance data-driven agricultural decision-making. Datasets from the Ministry of Agriculture and the Central Bureau of Statistics were integrated, transformed, and visualized using Microsoft Power BI, with a focus on evaluating fluctuations in harvested area, production volume, and productivity. Key objectives included identifying challenges linked to fragmented data and external disruptions. An Extract-Transform-Load (ETL) framework harmonized pre-2023 and post-2023 datasets, enabling standardized comparisons across 38 provinces. Results indicated a production peak of 486,000 tons in 2022, followed by a 4.5% decline in 2023 due to adverse climatic conditions and supply chain instability, and partial recovery to 15.2 million tons in 2024. Pronounced regional disparities emerged:  West Java recorded 80 quintals per hectare productivity, while urbanized regions like Jakarta reported negligible output. The analysis underscores the efficacy of Business Intelligence (BI) tools in converting raw agricultural data into strategic insights, offering policymakers pathways to optimize resource allocation, mitigate inequities, and strengthen climate-resilient practices. These outcomes highlight BI’s transformative potential in advancing sustainable agricultural development and adaptive governance frameworks.

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Published

2025-03-31

How to Cite

Adesyahputra, M. K., & Rachmawati, E. P. (2025). Analysis of corn production in Indonesia using business intelligence technology based on Power BI. Matrix : Jurnal Manajemen Teknologi Dan Informatika, 15(1), 21–31. https://doi.org/10.31940/matrix.v15i1.21-31