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ANALISIS FAKTOR RISIKO GAGAL JANTUNG DENGAN REGRESI LOGISTIK BERBASIS IoMT Arisandi, Rizwan; Dewi, Adhe Lingga
Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.4.549-559

Abstract

Technology in the era of revolution 4.0, which is currently developing so rapidly, has given birth to Internet of Things technology and can be implemented in the health sector or called the Internet of Medical Things (IoMT). IoMT technology can be applied to monitor heart disease patients and obtain medical record data that is useful for further decision making, such as predicting the potential for heart disease using logistic regression. This study uses medical record data for heart disease with the variable heart failure as the dependent variable and the variables age, gender, diabetes, anemia, hypertension, smoking habits as independent variables. In this research, machine learning was applied with a logistic regression algorithm on clinical data collected via IoMT devices to detect heart disease. Classification. The accuracy of the model was obtained at 75%, so it can be said that the model score is on the average model scale, which means the model is quite good. The average gender of patients who suffer a heart attack is male with an age range of 60-70 years. Furthermore, in patients who have a history of hypertension, a person's risk of developing heart failure increases by 4,2%. Meanwhile, in patients who have a history of diabetes, a person's risk of developing heart failure increases by 4%.
Magie Broom: Revolutionizing Cleaning with User-Centered Ergonomic Design Nafi'ah, Roikhanatun; Dewi, Adhe Lingga; Rosya, Kamila Nur
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.6850

Abstract

Magie Broom is an innovative cleaning tool designed by considering several principles such as ergonomics and anthropometry. This study aims to determine the optimal ergonomics design that can minimize the risk of musculoskeletal disorders (MSDs) that often arise from the use of traditional cleaning tools. The development of this tool involves several stages: literature review, field observations, and detailed design using the Quality Function Deployment (QFD) method, followed by testing. Anthropometric principles, especially for determining the optimal length and grip, are carefully considered to ensure the design meets user needs for ease of use, comfort, and cleaning effectiveness. The design The Magie Broom prototype was tested by various user groups such as housewives and students to obtain input used in refining the design. The test results showed that this tool was able to increase cleaning efficiency by provide 3in1 function and modularity. Its also provide comfort for users and reduce the potential for MSDs by decrease REBA score from 11 to 3. The design is very easy to carry, modular, and equipped with a rechargeable battery. The Magie Broom serves as a promising model for ergonomic product development especially in houshold tools, illustrating how thoughtful design can minimize physical strain and injury risk. Magie Broom offers a practical solution for everyday cleaning needs and has great potential to be further developed and marketed widely.