Effendi, Yutika Amelia
Universitas Airlangga

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

The application of instrumentation system on a contactless robotic triage assistant to detect early transmission on a COVID-19 suspect Niko Azhari Hidayat; Prisma Megantoro; Abdufattah Yurianta; Amila Sofiah; Shofa Aulia Aldhama; Yutika Amelia Effendi
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1334-1344

Abstract

This article discusses the instrumentation system of airlangga robotic triage assistant version 1 (ARTA-1), a robot used as a contact-free triage assistant for Coronavirus disease (COVID-19) suspects. The triage process consists of automatic vital signs check-up as well as the suspect’s anamnesis that in turns will determine whether the suspect will get a specific care or not. Measurements of a suspect’s vital conditions, i.e. temperature, height, and weight, are carried out with sensors integrated with the Arduino boards, while a touch-free, hand gesture questions and answers is carried out to complete anamnesis process. A portable document format (PDF) format of the triage report, which recommends what to do to the suspect, will then be automatically generated and emailed to a designated medical staff.
Predicting vulnerability for brain tumor: data-driven approach utilizing machine learning Effendi, Yutika Amelia; Sofiah, Amila; Hidayat, Niko Azhari; Ebrie, Awol Seid; Hamzah, Zainy
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1579-1589

Abstract

Brain tumors, whether benign or malignant, present a complex and multifaceted challenge in healthcare, affecting individuals across various age groups. Predicting the vulnerability of brain tumors using health risk factors and symptoms is crucial, yet there have been limited research studies, particularly those integrating artificial intelligence (AI) technology. This research explores machine learning models such as support vector machines (SVMs), multi-layer perceptrons (MLPs), and logistic regression (LR) for the early detection of brain tumors. Evaluation metrics, including accuracy, precision, recall, and F1-score, are employed to assess model performance. The results indicate that the SVM outperforms other models, providing a robust foundation for predictive accuracy. To enhance accessibility and usability, the research also integrates these models into a mobile application predictor. The application is beneficial for assisting individuals in early detection by identifying potential risk factors and symptoms that may lead to a brain tumor. In conclusion, the integration of machine learning through a mobile application represents a transformative approach to personalized healthcare. By empowering individuals with cutting-edge technology, this research strives to enhance early detection and decision-making regarding potential brain tumor risks and symptoms, ultimately contributing to improved patient outcomes and quality of life.