IoT Sensor Data Analysis for Early Fire Detection Using Dynamic Threshold

Authors

  • Widia Br Tarigan STMIK Time Medan
  • Robet Department of Informatics, STMIK Time, Medan, Indonesia
  • Feriani Astuti Tarigan Department of Informatics, STMIK Time, Medan, Indonesia

DOI:

10.33395/sinkron.v10i1.15478

Keywords:

IoT, dynamic threshold, Fuzzy Logic, machine learning, Adaptive Z-Score

Abstract

Early fire detection using Internet of Things (IoT) technology plays a vital role in minimizing potential material losses and casualties. Conventional systems generally still rely on static thresholds that are less adaptive to environmental dynamics, leading to high false alarm rates and delayed detection. This study proposes a dynamic threshold approach based on a hybrid method of Fuzzy Logic–Random Forest–Adaptive Z-Score and compares it with the static threshold method. Testing was conducted using publicly available secondary datasets, and the algorithms were implemented and tested in Jupyter Notebook. Evaluation was performed using accuracy, false alarm rate (FAR), detection time, F1-score, precision, and recall metrics. The test results show that the dynamic threshold method provides better performance with an increase in accuracy from 59.5% to 74.8%, a decrease in FAR from 31.1% to 14.3%, and a reduction in detection time from 21 seconds to 0 seconds. In addition, the F1-score increased from 0.459 to 0.638, precision from 0.473 to 0.716, and recall from 0.446 to 0.575. These results show that the dynamic threshold approach is more adaptive and reliable in IoT-based fire detection systems than conventional static threshold methods.

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How to Cite

Br Tarigan, W., Robet, R., & Tarigan, F. A. . (2026). IoT Sensor Data Analysis for Early Fire Detection Using Dynamic Threshold. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(1), 200-210. https://doi.org/10.33395/sinkron.v10i1.15478

Issue

Vol. 10 No. 1 (2026): Article Research January 2026

Section

Articles

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Copyright (c) 2025 Widia Br Tarigan, Robet, Feriani Astuti Tarigan

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

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