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All Journal Elektron Jurnal Ilmiah Journal of Applied Electrical Engineering The Indonesian Journal of Computer Science
Gusti, Agrippina Waya Rahmaning
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : The Indonesian Journal of Computer Science

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Sistem Screening Mandiri Covid-19 Berbasis Internet of Things (IoT) Gusti, Agrippina Waya Rahmaning; Rokhana, Rika; Kemalasari; Ningsih, Vita Kusuma
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3305

Abstract

The increasing need to perform Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) during the COVID-19 pandemic is time-consuming and costly. An IoT-based self-screening system of COVID-19, is expected to be a solution. In this study, instruments were created to monitor SpO2 and heart rate using the MAX30102 sensor, body temperature using the MLX90614 sensor, and breathing rate using a flex sensor as a COVID-19 detection parameter. The measured vital sign results are also transmitted to the website using the Wi-Fi module ESP32. The method used is a decision tree that has two classifications, indicated healthy and indicated COVID-19. Decision tree accuracy is 90% and recall is 100%. Temperature readings have a 98.68% accuracy, SpO2 readings and heartbeats are 98.74% and 98.64%, respectively, and breathing rate per minute readings are 100% accurate. This autonomous COVID-19 screening system can be used as an alternative solution for early detection of COVID-19.
Rancang Bangun Alat Ukur Kadar Gula Darah, Kolestrol, dan Asam Urat Non-Invasif Berbasis Internet of Things (IoT) Gusti, Agrippina Waya Rahmaning; Kemalasari; Rochmad, Mochammad; Az Zahro, Fatimah
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3532

Abstract

Diabetes and cardiovascular disease are examples of non-communicable diseases with the highest prevalence. One way to prevent this deadly disease is by carrying out regular health checks. Blood sugar, cholesterol and uric acid level detection devices on the market are invasive, causing discomfort with needles when drawing blood. The aim of this research is to design a tool to measure blood sugar, cholesterol and uric acid levels non-invasively using the MAX30105 sensor. The MAX30105 sensor will read the infrared value on the fingertip which is then converted into a calculation of blood sugar, cholesterol and uric acid values using the linear regression method. This method will compare the IR value from the sensor output with the results of invasive readings of glucose, cholesterol and uric acid levels to form a regression equation. The measurement data will then be sent via WiFi to the application for easy reading. Based on the experimental results, the accuracy value for glucose reading was 91.44%, cholesterol 84.94% and uric acid 84.91%. More sample variations are needed to map variables that can cause errors in reading values. Apart from that, the amount of light and finger placement procedures also need to be considered to get better accuracy.
Co-Authors Ardhana, Aliya Mitha Ardik Wijayanto Az Zahro, Fatimah Kemalasari Mochammad Rochmad, Mochammad Ningsih, Vita Kusuma Rokhana, Rika Rosalinda, Hanny Megawati Salim, Rizki Umar