Implementation of Blackbox Testing in Real-Time Thermal Video Detection Applications (Case Study of Fever/Covid-19 Detection)


Kukuh Yudhistiro Aditya Galih Sulaksono Aditya Hidayat Pratama


During the emergence of the Covid-19 pandemic whose vaccines have not been spread evenly, all countries in the world, especially Indonesia have taken several preventive steps to prevent the spread of the virus. One of the initial actions is to detect every person entering and leaving the country through airports or land transportation. This early action was carried out by detecting the body temperature of residents passing in and out of locations such as airports and train stations. The fever detection is generally carried out using a thermal gun in the form of an infrared gun aimed at individuals who pass the inspection. This research discusses a series of tools consisting of a camera with a thermal sensor where the captured data will be processed through software that displays a histogram of the temperature from the chest to the person's head in real time. Each capture result is used as a dataset that can be used for tracing the needs of visitors to public places. In this research, we will discuss functional testing (blackbox) of the application of thermal video detection in case studies of fever detection.


[1] G. Batchuluun, D. T. Nguyen, T. D. Pham, C. Park, and K. R. Park, “Action recognition from thermal videos,” IEEE Access, vol. 7, pp. 103893–103917, 2019, doi: 10.1109/ACCESS.2019.2931804.
[2] Https://, “Three Common Misconceptions About Infrared Thermometers,” 2012. diakses pada 2 Juni 2021.
[3] D. J. Mamahit, “Detection Early Breast Cancer By Using Digital Infrared Image Based on Asymmetry Thermal,” Detect. Early Breast Cancer By Using Digit. Infrared Image Based Asymmetry Therm., pp. 1–8, 2014.
[4] M. S. Jadin, S. Taib, and S. Kabir, “Infrared thermography for assessing and monitoring electrical components within concrete structures,” Prog. Electromagn. Res. Symp., no. February 2016, pp. 786–789, 2011.
[5] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 580–587, 2014, doi: 10.1109/CVPR.2014.81.
[6] S. Taib, M. Shawal, and S. Kabir, “Thermal Imaging for Enhancing Inspection Reliability: Detection and Characterization,” Infrared Thermogr., no. February 2015, 2012, doi: 10.5772/27558.
[7] M. Shi, “Software Functional Testing from the Perspective of Business Practice,” Comput. Inf. Sci., vol. 3, no. 4, pp. 49–52, 2010, doi: 10.5539/cis.v3n4p49.
[8] R. Jampani, N. Talasu, and R. Manjula, “Survey of Software Testing Techniques,” no. April, 2016.
[9] N. Anwar and S. Kar, “Review Paper on Various Software Testing Techniques & Strategies,” Glob. J. Comput. Sci. Technol., vol. 19, no. 2, pp. 43–49, 2019, doi: 10.34257/gjcstcvol19is2pg43.
[10] M. M. Syaikhuddin, C. Anam, A. R. Rinaldi, and M. E. B. Conoras, “Conventional Software Testing Using White Box Method,” Kinetik, vol. 3, no. 1, p. 67, 2018, doi: 10.22219/kinetik.v3i1.231.
[11] K. Mohd. Ehmer and K. Farmeena, “A Comparative Study of White Box , Black Box and Grey Box Testing Techniques,” Int. J. Adv. Comput. Sci. Appl., vol. 3, no. 6, pp. 12–15, 2012, doi: 10.1017/CBO9781107415324.004.
[12] C. Baek, J. Jang, G. Jung, K. Choi, and S. Park, “A Case Study of Black-Box Testing for Embedded Software using Test Automation Tool,” J. Comput. Sci., vol. 3, no. 3, pp. 144–148, 2007, doi: 10.3844/jcssp.2007.144.148.
[13] A. June, A. Sgvu, and V. Chandra, “Fuzzy Theory in Black Box Testing,” Int. J. Adv. Res. Comput. Sci. Technol., vol. 2, no. 2, pp. 289–291, 2014.
[14] S. Nidhra, “Black Box and White Box Testing Techniques - A Literature Review,” Int. J. Embed. Syst. Appl., vol. 2, no. 2, pp. 29–50, 2012, doi: 10.5121/ijesa.2012.2204.
[15] T. Hidayat and M. Muttaqin, “Pengujian Sistem Informasi Pendaftaran dan Pembayaran Wisuda Online menggunakan Black Box Testing dengan Metode Equivalence Partitioning dan Boundary Value Analysis,” J. Tek. Inform. UNIS JUTIS, vol. 6, no. 1, pp. 2252–5351, 2018, [Online]. Available:
[16] T. F. Gonzalez, “Handbook of approximation algorithms and metaheuristics,” Handb. Approx. Algorithms Metaheuristics, pp. 1–1432, 2007, doi: 10.1201/9781420010749.
[17] C. Szegedy, S. Reed, D. Erhan, D. Anguelov, and S. Ioffe, “Scalable, High-Quality Object Detection,” 2014, [Online]. Available: