Penerapan Optical Character Recognition Proses Registrasi Pasien Tes Covid-19 Berbasis Web
Indonesia
-
I Putu Gede Abdi Sudiatmika
*
Institut Teknologi dan Bisnis STIKOM Bali, Denpasar,
Indonesia
- Ni Komang Hari Santhi Dewi Institut Teknologi dan Bisnis STIKOM Bali, Denpasar, Indonesia
- I Komang Okky Suardhana Institut Teknologi dan Bisnis STIKOM Bali, Denpasar, Indonesia
- Nyoman Pradipta Dewantara Institut Teknologi dan Bisnis STIKOM Bali, Denpasar, Indonesia (*) Corresponding Author
Abstract
Covid-19 cases in Bali in the period October to November 2020 experienced an increase which could cause hospitals to experience an increase in demand for health services. Hospitals are one of the most important departments during the Covid-19 pandemic. As a health service agency, the hospital must provide quality services to all levels of society who visit. In realizing optimal public health services, hospitals need a system that supports services to the community. In carrying out the Covid-19 test, the patient registers at the admission, the Nakes (Health Personnel) enters the patient's identity manually using the Microsoft Excel application. In the registration process, there are often discrepancies in the patient's name, date of birth, address and telephone number as well as other identities due to input errors. The large number of patients who register to do the Covid-19 test makes the Nakes (Health Personnel) take a long time, the time required to collect the data is 10 minutes and results in a queue buildup. To overcome this, applying Optical Character Recognition (OCR) can make it easier for Nakes (Health Personnel) to register and input KTP / KIS data automatically, thereby reducing patient data collection errors. Seeing this problem, from this research, a website was built by applying Optical Character Recognition (OCR) and the development method used was the Waterfall method and the suitability of the system built was tested using the black box testing method. This system gets a percentage of 79.4% which means both from admin and doctor respondents, and gets a percentage of 92.8% which means very good from user respondents (patients).
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