Mobile Application to Warning Computer Vision Syndrome (CVS) Risk by Utilizing Ambient Light Sensor
Nadya Fitri Fajriyah, Dihin Muriyatmoko, Resi Utami Putri

Universitas Darussalam Gontor

Abstract

Computer Vision Syndrome (CVS) it is also known as digital eye strain. American Optometric Association (AOA) define it as a group of problems related to vision and eyes caused by prolonged use of gadgets. Among the causes of CVS are poor lighting and glare on digital screens. Adjusting lighting can reduce glare on the screen and improve visual comfort and eye performance. Conversely, if we use a smartphone screen that is too bright in a dimmer room will accelerate fatigue in the eyes. Habits of smartphone users who use it without regard to the brightness level of the room that can increase the risk of exposure to CVS. The purpose of this research is to early warning the risk of exposure to CVS. This research builds a mobile application as a reminder that the brightness level of the room is below the standard range set by OSHA, NEQS, and PERMENKES No.70 of 2016 which is at least 250 lx and 300 lx. Blackbox test results showed that apps can run without error, and functionality test results show that apps can warn if the brightness level of the room is below 250 lux and exceeds 1000 lux.

Keywords: Health apps- Computer Vision Syndrome- Ambient Light Sensor- Early Warning Mobile Apps- Lux Meter

Topic: Computer Science