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All Journal Journal of Applied Engineering and Technological Science (JAETS) Journal of Multiscale Materials Informatics
Adhe Lingga Dewi
Bina Nusantara University
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Investigation of an amino acid compound as a corrosion inhibitor via ensemble learning Adhe Lingga Dewi; Muhamad Akrom
Journal of Multiscale Materials Informatics Vol. 1 No. 2 (2024): October
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i2.11053

Abstract

In this study, we evaluate the performance of various machine learning models, including Random Forest (RF), Bagging (BAG), AdaBoost (ADA), Artificial Neural Network (ANN), and Support Vector Machine (SVM), using metrics such as R², Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results indicate that AdaBoost (ADA) achieves the highest performance with an R² of 0.999, RMSE of 2.32, and MAE of 2.24, making it the most accurate model with the smallest prediction errors. Bagging (BAG) also performs exceptionally well, with an R2 of 0.996, RMSE of 3.09, and MAE of 2.92. The Artificial Neural Network (ANN) exhibits a high R2 of 0.999, though RMSE and MAE values are not provided. Random Forest (RF) and Support Vector Machine (SVM) show good performance with R² values of 0.982 and 0.970, respectively, but are outperformed by the ensemble methods. The findings underscore the superiority of ensemble techniques, particularly AdaBoost, in achieving high predictive accuracy and minimal errors in this context.
Magie Broom: Revolutionizing Cleaning with User-Centered Ergonomic Design Roikhanatun Nafi'ah; Adhe Lingga Dewi; Kamila Nur Rosya
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.
Co-Authors Kamila Nur Rosya Muhamad Akrom Roikhanatun Nafi'ah