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Submitted
Feb 16, 2026
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Mar 27, 2026
Published
Mar 31, 2026
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Abstract

Multi-sensor technology based on the Internet of Things (IoT) is implemented in determining the accuracy of sensors for air temperature, air humidity, and light intensity. The multi-sensor based on IoT consists of a DHT11 sensor to detect air temperature and humidity, and a BH1750 sensor to detect light intensity was implemented using the NodeMCU ESP8266 microcontroller, monitored via the IoT on the Blynk application on the smartphone. Data Sensor validation by comparing data from the SNI measuring instrument to determine the sensor accuracy. The results of testing the sensors for air temperature, air humidity, and light intensity produced average accuracies of 99.899%, 100%, and 99.895%, respectively. All sensor errors remain within the specified tolerance limit of <5%. The low error sensor results in a high accuracy value. These sensors are suitable for various monitoring needs, both in automation systems and environmental research.

Keywords

Accuracy, air humidity, air temperature, internet of thing, light intensity, multi-sensor

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

How to Cite
1.
Yunus M, Yudoyono S, Setiawan DGE, Fatimah S, Amir A. Multi-Sensor Based on Internet of Thing (IoT) in Determining the Accuracy of Sensors for Air Temperature, Air Humidity, and Light Intensity. EKSAKTA [Internet]. 2026 Mar. 31 [cited 2026 Apr. 9];27(02):120-31. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/661

References

  1. [1] Ashraf, M. A., & Mohd Hanafiah, M. (2019). Sustaining life on earth system through clean air, pure water, and fertile soil. Environmental science and pollution research, 26(14), 13679-13680.
  2. [2] Smith, P., Ashmore, M. R., Black, H. I., Burgess, P. J., Evans, C. D., Quine, T. A., & Orr, H. G. (2013). The role of ecosystems and their management in regulating climate, and soil, water and air quality. Journal of Applied Ecology, 50(4), 812-829.
  3. [3] Ramani, D. R., Sujitha, B. B., & Tangade, S. (2025). Smart environmental monitoring systems: IoT and sensor‐based advancements. Environmental Monitoring Using Artificial Intelligence, 45-60.
  4. [4] Ullo, S. L., & Sinha, G. R. (2020). Advances in smart environment monitoring systems using IoT and sensors. Sensors, 20(11), 3113.
  5. [5] Chapman, J., Truong, V. K., Elbourne, A., Gangadoo, S., Cheeseman, S., Rajapaksha, P., & Cozzolino, D. (2020). Combining chemometrics and sensors: Toward new applications in monitoring and environmental analysis. Chemical Reviews, 120(13), 6048-6069.
  6. [6] Kruse, P. (2018). Review on water quality sensors. Journal of Physics D: Applied Physics, 51(20), 203002.
  7. [7] Narayana, T. L., Venkatesh, C., Kiran, A., Kumar, A., Khan, S. B., Almusharraf, A., & Quasim, M. T. (2024). Advances in real time smart monitoring of environmental parameters using IoT and sensors. Heliyon, 10(7).
  8. [8] Afriani, M., Hamdani, D., & Putri, D. H. (2024). Development of an Integrated Monitoring System for Temperature, Air Humidity, Soil Moisture, and Light Intensity in Measurement Aids in Secondary Schools. Edukasi: Jurnal Pendidikan dan Pengajaran, 11(02), 117-134.
  9. [9] Zhao, H., Ji, W., Deng, S., Wang, Z., & Liu, S. (2024). A review of dynamic thermal comfort influenced by environmental parameters and human factors. Energy and Buildings, 318, 114467.
  10. [10] Sung, W. T., Hsiao, S. J., & Shih, J. A. (2019). Construction of indoor thermal comfort environmental monitoring system based on the IoT architecture. Journal of Sensors, 2019(1), 2639787.
  11. [11] Lee, G., Wei, Q., & Zhu, Y. (2021). Emerging wearable sensors for plant health monitoring. Advanced Functional Materials, 31(52), 2106475.
  12. [12] Mendell, M. J., Macher, J. M., & Kumagai, K. (2018). Measured moisture in buildings and adverse health effects: A review. Indoor air, 28(4), 488-499.
  13. [13] Sriyanti, I., Aliyana, P., Marlina, L., & Jauhari, J. (2020, February). Light intensity analysis using smartphone’s light sensor. In Journal of Physics: Conference Series (Vol. 1467, No. 1, p. 012056). IOP Publishing.
  14. [14] Hadj Abdelkader, O., Bouzebiba, H., Pena, D., & Aguiar, A. P. (2023). Energy-efficient IoT-based light control system in smart indoor agriculture. Sensors, 23(18), 7670.
  15. [15] Li, J., Fang, Z., Wei, D., & Liu, Y. (2024). Flexible pressure, humidity, and temperature sensors for human health monitoring. Advanced healthcare materials, 13(31), 2401532.
  16. [16] Anderson, V., Leung, A. C., Mehdipoor, H., Jänicke, B., Milošević, D., Oliveira, A., ... & Zurita-Milla, R. (2021). Technological opportunities for sensing of the health effects of weather and climate change: a state-of-the-art-review. International Journal of Biometeorology, 65(6), 779-803.
  17. [17] Farhan, K. Z., Shihata, A. S., Anwar, M. I., & Demirboğa, R. (2023). Temperature and humidity sensor technology for concrete health assessment: A review. Innovative Infrastructure Solutions, 8(10), 276.
  18. [18] Dutta, P. K., Singh, B., Towfeek, A. S. K., Adamopoulou, J. P., Bardavouras, A. N., Bamwerinde, W., ... & Ayiga, N. (2024). IoT revolutionizes humidity measurement and management in smart cities to enhance health and wellness. Mesopotamian Journal of Artificial Intelligence in Healthcare, 2024, 110-117.
  19. [19] Jiang, J., Moallem, M., & Zheng, Y. (2021). An intelligent IoT-enabled lighting system for energy-efficient crop production. Journal of daylighting, 8(1), 86-99.
  20. [20] Ghiasi, M., Wang, Z., Mehrandezh, M., & Paranjape, R. (2023). A systematic review of optimal and practical methods in design, construction, control, energy management and operation of smart greenhouses. IEEE access, 12, 2830-2853.
  21. [21] Fattah, G., Mabrouki, J., Ghrissi, F., Azrour, M., & Abrouki, Y. (2022). Multi-sensor system and internet of things (IoT) technologies for air pollution monitoring. In Futuristic research trends and applications of Internet of Things (pp. 101-116). CRC Press.
  22. [22] Liu, Q. (2020). Intelligent environmental monitoring system based on multi-sensor data technology. International Journal of Ambient Computing and Intelligence (IJACI), 11(4), 57-71.
  23. [23] Wu, F., Wu, T., & Yuce, M. R. (2018). An internet-of-things (IoT) network system for connected safety and health monitoring applications. Sensors, 19(1), 21.
  24. [24] Simic, M. (2013, September). Microcontroller based system for measuring and data acquisition of air relative humidity and temperature. In 37th International Conference of IMAPS-CPMT Poland, Kraków (pp. 22-25).
  25. [25] Sari, Z. (2017). Prototype Sistem Multi-Telemetri Wireless untuk Mengukur Suhu Udara Berbasis Mikrokontroler ESP8266 pada Greenhouse. KINETIK.
  26. [26] Pereira, P. F., & Ramos, N. M. (2022). Low-cost Arduino-based temperature, relative humidity and CO2 sensors-An assessment of their suitability for indoor built environments. Journal of Building Engineering, 60, 105151.
  27. [27] Rianti, K. P. K., & Prastyo, Y. (2022). Analisis Penggunaan Sensor Suhu Dan Kelembaban Untuk Monitoring Lingkungan Greenhouse Berbasis Arduino. Antivirus: jurnal ilmiah teknik informatika, 16(2), 200-210.
  28. [28] Beyaz, A., & Gül, V. (2022). Determination of low-cost arduino based light intensity sensors effectiveness for agricultural applications. Brazilian Archives of Biology and Technology, 65, e22220172.
  29. [29] Nanda, R. A., Karyadi, K., & Dewadi, F. M. (2022). Pengukuran Intensitas Cahaya Menggunakan Sensor BH-1750 Berbasis Mikrokontroler: Studi Kawasan Kampus UBP Karawang. Praxis: Jurnal Sains, Teknologi, Masyarakat Dan Jejaring, 5(1), 74-81.
  30. [30] Deqita, A. (2022). Artikel analisis intensitas radiasi matahari dan peningkatan suhu lingkungan. Jurnal Pendidikan Fisika dan Sains (JPFS), 5(2), 76-82.
  31. [31] Tiyas, A. W., Erwanto, D., Yanuartanti, I., Elektro, T., & Kadiri, U. I. (2025). Peningkatan Akurasi Sensor Suhu dan Kelembaban DHT11 dengan Kalibrasi Suhu Berbasis IoT pada Platform Thingspeak. Jurnal Pendidikan dan Teknologi Indonesia, 5(3), 625-633.
  32. [32] Hadi, S., Labib, R. P. M. D., & Widayaka, P. D. (2022). Perbandingan Akurasi Pengukuran Sensor LM35 dan Sensor DHT11 untuk Monitoring Suhu Berbasis Internet of Things. STRING (Satuan Tulisan Riset dan Inovasi Teknologi), 6(3), 269-278.
  33. [33] Karyati, K., Putri, R. O., & Syafrudin, M. (2018). Suhu dan kelembaban tanah pada lahan revegetasi pasca tambang di PT Adimitra Baratama Nusantara, Provinsi Kalimantan Timur. AGRIFOR: Jurnal Ilmu Pertanian dan Kehutanan, 17(1), 103-114.
  34. [34] Nelvi, A., & Nata, R. A. (2023). Durasi Penyinaran Matahari Dan Diurnal Temperature Range Serta Kaitannya Dengan Perubahan Iklim Di Pontianak, Indonesia. Jurnal Meteorologi dan Geofisika, 24(2), 65-76.
  35. [35] Handayani, Y. S., Gulo, W., & Priyadi, I. (2024). Analisa Sistem Kerja Sensor Encoder dan Sensor Load Cell pada Pengemasan Semen di PT. Cemindo Gemilang Plant Bengkulu. Applied Engineering, Innovation, and Technology, 1(1), 31-38.
  36. [36] Rustami, E., Adiati, R. F., Zuhri, M., & Setiawan, A. A. (2022). Uji karakteristik sensor suhu dan kelembaban multi-channel menggunakan platform Internet of Things (IoT). Berkala Fisika, 25(2), 45-52.
  37. [37] Soneye, O. O., Ayoola, M. A., Ajao, I. A., & Jegede, O. O. (2019). Diurnal and seasonal variations of the incoming solar radiation flux at a tropical station, Ile-Ife, Nigeria. Heliyon, 5(5).
  38. [38] Moradi, I., Arkin, P., Ferraro, R., Eriksson, P., & Fetzer, E. (2016). Diurnal variation of tropospheric relative humidity in tropical regions. Atmospheric Chemistry and Physics, 16(11), 6913-6929.
  39. [39] Salamone, F., Chinazzo, G., Danza, L., Miller, C., Sibilio, S., & Masullo, M. (2022). Low-cost thermohygrometers to assess thermal comfort in the built environment: A laboratory evaluation of their measurement performance. Buildings, 12(5), 579.
  40. [40] Kartika, K., Jannah, M., Aulia, R., & Misriana, M. (2025). Implementation of Linear Regression Method in Light Strength Measurement Using GY1750BH Sensor. Faktor Exacta, 18(1), 32-41.
  41. [41] Lu, L., Li, Y., Liang, L., & Ma, Q. (2024). Diurnal Variation in Surface Incident Solar Radiation Retrieved by CERES and Himawari-8. Remote Sensing, 16(14), 2670.