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All Journal International Journal of Mechanical Engineering Technologies and Applications (MECHTA)
Harmayanti, Afifah
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Automotive Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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CARDIAC BIOMETRICS AND PERCEIVED WORKLOAD REGRESSION ANALYSIS USING RANDOM FOREST REGRESSOR IN COGNITIVE MANUFACTURING TASKS Harmayanti, Afifah; Tama, Ishardita Pambudi; Gapsari, Femiana; Akbar, Zuardin; Juliano, Hans
International Journal of Mechanical Engineering Technologies and Applications Vol. 5 No. 1 (2024)
Publisher : Mechanical Engineering Department, Engineering Faculty, Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/MECHTA.2024.005.01.11

Abstract

Workload is crucial in managing and maintaining good performance of human resources and allocations. In an advanced manufacturing industry, human job functions had shifted to cognitive tasks. Thus, cognitive workload evaluation should be used to monitor worker’s workload in optimal condition. Most common tool of cognitive workload tools are perceived measurement, like NASA – TLX questionnaire. Despite of its sensitivity to capture workload felt by the workers, this subjective measurement was prone to bias. Objective measurement utilizing biometrics data of the human body during working state was useful to eliminate bias. Cardiac biometrics were one of the many that were closely related to mental activity changes. The objective of this study was to understand the relationship of cardiac biometrics to perceived workload as an indicator of cognitive workload analysis. The study utilized four biometrics, heart rate, HRV low frequency power, total frequency power and ratio of low and high frequency power, were used to analyzed a one hour long cognitive based study case. The study case was designed in a manufacturing planning context referring to manufacturing aptitude tests, to induce cognition process on 30 participants. The biometrics and NASA – TLX score result of all the participants, were then calculated as effect size standardization before input into random forest regressor model to analyze relationship between cardiac biometrics and perceived workload. The result found a moderate relationship between the two (r2 = 0.576). Features importance also showed the most impactful feature to the model is the effect size of frequency power ratio. However, it is recommended to always consider evaluating multiple cardiac biometrics in workload analysis to ensure good model performance.
SUPERHYDROPHOBIC AND ANTIBACTERIAL COATINGS ON VARIOUS COTTON FABRICS USING ZNO AND AESO Wijaya, Hastono; Gapsari, Femiana; Sulaiman, Abdul M.; Harmayanti, Afifah; Barasa, Alvadro; Andrean, Janu; Warman, Sa Bashkaran Adi; Kriswardhana, Willy; Naimah, Azimatun
International Journal of Mechanical Engineering Technologies and Applications Vol. 5 No. 2 (2024)
Publisher : Mechanical Engineering Department, Engineering Faculty, Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/MECHTA.2024.005.02.12

Abstract

Superhydrophobic coatings on cotton utilized in medical applications like hospital gowns and bed linens to offer a protective barrier against fluids and bacteria. Masks were worn with different types of materials. In this study, various cotton employed ZnO and AESO to effectively decrease the surface energy of cotton fabric via a Schiff base reaction. This chemical transformation resulted in the formation of a textured surface structure that exhibited robust adhesion qualities. The study demonstrates that the superhydrophobic coating on silk fabric increases 153. 59%. The coating on silk provides a reference for fabric types with ZnO and AESO coatings.
Co-Authors Akbar, Zuardin Andrean, Janu Barasa, Alvadro Femiana Gapsari MF Ishardita Pambudi Tama Juliano, Hans Naimah, Azimatun Sulaiman, Abdul M. Warman, Sa Bashkaran Adi Wijaya, Hastono Willy Kriswardhana