Integration of Machine Learning and Blockchain for Forest Fire Risk Prediction

Authors

  • Nursetiaji Ramadhani Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional, Jakarta
  • Ira Diana Sholihati Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional, Jakarta
  • Djarot Hindarto Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional, Jakarta
  • Asrul Sani Magister Teknologi Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional

DOI:

10.33395/sinkron.v9i4.15435

Keywords:

Forest Fire Prediction, Machine Learning, Random Forest, XGBoost, Smart Contract

Abstract

This study presents an integrated framework combining machine learning and blockchain technology to enhance the accuracy, transparency, and reliability of forest fire risk prediction in tropical regions. Using geospatial and climatological datasets from Google Earth Engine (GEE), two ensemble algorithms—Random Forest (RF) and Extreme Gradient Boosting (XGBoost)—were trained to model spatial fire susceptibility based on variables such as temperature, humidity, rainfall, wind speed, and vegetation index (NDVI). The RF model effectively identified low-risk areas but was less sensitive to minority high-risk classes, while XGBoost demonstrated superior adaptability in handling class imbalance and achieved more balanced performance across all categories. To ensure data authenticity and traceability, the prediction results were validated and recorded on the Ethereum blockchain using smart contracts. Each prediction output was transformed into a cryptographic hash (SHA-256) to guarantee immutability and verifiability. The integration of machine learning with blockchain establishes a decentralized, tamper-proof, and verifiable prediction system, promoting data integrity and transparency in environmental monitoring. Overall, this research introduces a novel “verifiable prediction pipeline” that advances both artificial intelligence and blockchain applications in environmental informatics, supporting proactive and accountable forest fire mitigation strategies.

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References

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How to Cite

Ramadhani, N. ., Sholihati, I. D. ., Hindarto, D. ., & Sani, A. (2025). Integration of Machine Learning and Blockchain for Forest Fire Risk Prediction. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(4), 3356-3364. https://doi.org/10.33395/sinkron.v9i4.15435

Issue

Vol. 9 No. 4 (2025): Articles Research October 2025

Section

Articles

License

Copyright (c) 2025 Nursetiaji Ramadhani, Ira Diana Sholihati, Djarot Hindarto

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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