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All Journal SANITAS: Jurnal Teknologi dan Seni Kesehatan International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Vita Nurdinawati
Poltekkes Kemenkes Jakarta II
Computer Science & IT Electrical & Electronics Engineering Environmental Science Health Professions Materials Science & Nanotechnology Medicine & Pharmacology Public Health

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Design Of A Vitiligo Home Phototherapy Using Narrow Band Ultraviolet-B (NB-UVB) Based On Arduino Uno Atika Hendryani; Hazzie Zati Bayani; Vita Nurdinawati
SANITAS: Jurnal Teknologi dan Seni Kesehatan Vol 13 No 2 (2022): SANITAS Volume 13 Nomor 2 Tahun 2022
Publisher : Politeknik Kesehatan Kemenkes Jakarta II

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36525/sanitas.2022.12

Abstract

Vitiligo is one of the skin diseases where there is a disorder in skin pigmentation (skin color disorder), which is characterized by the presence of macula hypopigmentation (a condition that causes part of the skin color to become lighter than the surrounding skin color) caused by the loss of melanocyte function (a special cell found under the skin epidermis that functions to produce skin pigments/melanin) chronically and progressively from the epidermis. This disease if left untreated can spread to other parts of the body. Phototherapy is a method to help reduce the spread of spotting on the body by using NB-UVB rays. This therapy should be carried out on an ongoing basis. To reduce contact with others during the Covid-19 pandemic and there is a vitiligo disease therapy protocol, the author designed the Home Phototherapy Vitiligo device. The author made this device by using NB-UVB lamps and HC-SR04 sensors to detect the exposure distance of 3 cm. The radiation dose starts from 150 mJ/cm2 – 3000 mJ/cm2. The author measured the irradiation of the device obtained 2.6 mW/cm2 and then converted to a radiation dose formula to get how long the therapy time was. The therapy time is obtained level 1 (58 seconds) – level 22 (1085 seconds). In this device obtained the accuracy of the therapy time at the 58th second, the 540th second and the 1085th second is 99.99%. For the value of the accuracy of the distance at 1 cm is 99.82%, at a distance of 2 cm is 99.96%, at a distance of 3 cm is 99.33%, at a distance of 5 cm is 99.944%.
Implementation of Thermal Camera for Human Stress Detection: A Review Atika Hendryani; Vita Nurdinawati; Andy Sambiono
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.28

Abstract

Stress has become a major problem that people face today. The high level of competition and environmental demands make people more susceptible to stress. Stress can interfere with a person's ability to work effectively. If left unchecked for a long time, stress can cause various dangerous diseases such as hypertension, heart problems, and others that can lead to death. Research has been conducted for a long time to detect stress. Various technologies have been used to detect and anticipate stress that occurs in humans. One promising technology for detecting stress is the use of thermal cameras. Thermal cameras have several advantages: being non-contact and non-invasive, quick, easy to use, and cost-effective. In general, the architecture of the stress detection system using a thermal camera consists of several stages, including image acquisition, pre-processing, ROI tracking and selection, feature extraction, and statistical analysis or classification. This paper aims to review the use of thermal cameras in detecting stress in humans. This paper also seeks to answer the research question of what analysis can be done to improve stress detection accuracy using thermal camera images. Research shows that ROI selection must be carefully considered to obtain good accuracy. Combining thermal images with other data can improve accuracy in stress detection. Machine learning in classification provides many benefits in recognizing patterns but is highly influenced by the number of datasets used.
Co-Authors Andy Sambiono Atika Hendryani Atika Hendryani Hazzie Zati Bayani