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The Indonesian Food and Beverages (F&B) industry adheres to high quality standards to maintain product quality. This paper aims to develop a modular device for non-contact temperature monitoring, preventing cross-contamination. The device uses an MLX90614 infrared temperature sensor and records temperature data digitally. The project includes the development of modular software and hardware, including infrared temperature gun and docking stations integrated with Thingsboard. Implementation focuses on raw materials such as milk, bread and sausages. The methodology in this project includes literature study, experimentation, calibration, and data analysis. Experiments with 20 temperature measurements for each test and raw material. Data is analyzed statistically to evaluate device performance, including emissivity calibration, optimal measurement distance, data storage capabilities on the device, and the effect of object temperature on sensor readings. The results show sensor calibration with emissivity values ​​of 0.79 for milk, 0.83 for bread, and 0.94 for sausage. The best accuracy is at a distance of 1 cm with an accuracy of 99.61% for milk, 99.79% for bread, and 89.23% for sausage. Using ESP32 DevKitC V4, the device stores eight temperature data and measurement times per mode. Thingsboard as a cloud platform is able to present temperature data and measurement times in three tables according to mode.

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Internet of Things (IoT) Based Temperature Monitoring System for the F&B Industry

  • 会议论文

  • 发表日期：

    2026年

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  • Santoso Budijono,
  • Rico Wijaya,
  • Vincent Harjadi,
  • Vendy Sanjaya Pranoto

摘要

The Indonesian Food and Beverages (F&B) industry adheres to high quality standards to maintain product quality. This paper aims to develop a modular device for non-contact temperature monitoring, preventing cross-contamination. The device uses an MLX90614 infrared temperature sensor and records temperature data digitally. The project includes the development of modular software and hardware, including infrared temperature gun and docking stations integrated with Thingsboard. Implementation focuses on raw materials such as milk, bread and sausages. The methodology in this project includes literature study, experimentation, calibration, and data analysis. Experiments with 20 temperature measurements for each test and raw material. Data is analyzed statistically to evaluate device performance, including emissivity calibration, optimal measurement distance, data storage capabilities on the device, and the effect of object temperature on sensor readings. The results show sensor calibration with emissivity values ​​of 0.79 for milk, 0.83 for bread, and 0.94 for sausage. The best accuracy is at a distance of 1 cm with an accuracy of 99.61% for milk, 99.79% for bread, and 89.23% for sausage. Using ESP32 DevKitC V4, the device stores eight temperature data and measurement times per mode. Thingsboard as a cloud platform is able to present temperature data and measurement times in three tables according to mode.