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All Journal Techno.Com: Jurnal Teknologi Informasi JOIV : International Journal on Informatics Visualization Journal of Applied Electrical Engineering Jurnal Rekayasa elektrika The Indonesian Journal of Computer Science Emitor: Jurnal Teknik Elektro
Rokhana, Rika
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Journal : The Indonesian Journal of Computer Science

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Penentuan Tingkat Stres berdasarkan Bio-Parameter Menggunakan Variasi Kernel Support Vector Machine Daffa Syah Alam; Rokhana, Rika; Arief, Zainal
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): 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.v13i6.4495

Abstract

System for detecting a person's stress level based on bio-parameters is blood pressure, heart rate, and respiratory rate. Measurements of blood pressure, heart rate, and respiratory rate in order to detect the condition of a person's stress level are carried out non-invasively or don’t damage the nervous tissue in the body and routinely. Heart rate measurement using MAX30102 sensor on the finger. Measurement of blood pressure using the MPX2050GP pressure sensor by placing cuff on the person's arm. While measuring the breathing rate using the MAX9814 micondensor sensor. In determining or classifying stress level conditions from non-invasive measurement parameters of blood pressure, heart rate and respiratory rate using Support Vector Machine (SVM) method with specified kernel variations. The classification of stress level conditions consists of four classes including normal, mild stress, moderate stress and severe stress. So that a dataset of 71 data is obtained with the data augmentation process and the accuracy of each SVM kernel variation used is obtained.
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.
Co-Authors Ardik Wijayanto Ari Setiawan Daffa Syah Alam Gusti, Agrippina Waya Rahmaning Kemalasari Ningsih, Vita Kusuma Pratiwi, Reniantika Dwi Riyanto Sigit, Riyanto Salim, Rizki Umar Sanaba, Utari Setiawardhana, Setiawardhana Waya Rahmaning Gusti, Agrippina Zainal Arief