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All Journal Makara Journal of Science Jurnal Polimesin
Mulia, Vania Katherine
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Automotive Engineering Control & Systems Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Poly(2-Hydroxyethyl Methacrylate) Hydrogels for Contact Lens Applications–A Review Saptaji, Kushendarsyah; Iza, Nurlaely Rohmatul; Widianingrum, Sinta; Mulia, Vania Katherine; Setiawan, Iwan
Makara Journal of Science Vol. 25, No. 3
Publisher : UI Scholars Hub

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Abstract

The emerging technology in biomedical engineering requires biocompatible materials, which are also referred to as biomaterials. For a material to be considered biocompatible, it should not interact with human tissues in a harmful way, and vice versa. Various properties of biocompatible materials, such as mechanical and optical properties, have to be considered for different biomedical applications. One of the most popular applications of biomaterials is for contact lenses. Hydrogels, specifically poly(2-hydroxyethyl methacrylate) (PHEMA) hydrogels, are among the most popular ones in ophthalmologic applications, especially in soft contact lenses. This paper reviews the use of PHEMA hydrogels as one of the important biomaterials. The possible applications, properties, and manufacturing process of PHEMA hydrogels, especially in contact lens applications, are addressed. Many studies have shown that PHEMA hydrogels possess many advantages in contact lens applications and have promising development prospects.
Machine fault detection through sound analysis using MFCC and machine learning Chang, Steven Henderson; Purnomo, Ariana Tulus; Bhakti, Muhammad Agni Catur; Mulia, Vania Katherine; Rizky, Agyl Fajar; Fernandez, Nikolas Krisma Hadi; Triawan, Farid
Jurnal Polimesin Vol 23, No 3 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i3.6653

Abstract

This study addresses the need for automated damage and failure detection in industrial machinery through sound analysis and machine learning. Traditional methods rely on human experts to identify faults using microphones, which can be time-consuming, stressful, and prone to errors such as limited perception, subjectivity, and inconsistency. This study leverages machine learning to create a more objective and efficient alternative. Mel-Frequency Cepstral Coefficients (MFCCs) were employed for feature extraction, capturing intricate sound patterns associated with machinery faults. Through rigorous experimentation, 11 MFCC coefficients were identified as optimal. The Support Vector Machine (SVM) emerged as the best-performing classifier compared to LightGBM and XGBoost, achieving a training accuracy of 83.12% and testing accuracy of 82.50%. The dataset was split between 80% for training and 20% for testing. The small gap between training and testing accuracy indicates an ideal model with no signs of over fitting, under fitting, or data leakage. Real-world simulations validated the model’s efficacy under various operational scenarios, demonstrating its readiness for industrial deployment. This study highlights the effectiveness of sound analysis and SVM classification in proactive maintenance, offering a reliable tool to reduce downtime and maintenance costs while enhancing operational efficiency and reliability.
Co-Authors Ariana Tulus Purnomo Chang, Steven Henderson Fernandez, Nikolas Krisma Hadi Iwan Setiawan Iza, Nurlaely Rohmatul Kushendarsyah Saptaji Muhammad Agni Catur Bhakti Rizky, Agyl Fajar Triawan, Farid Widianingrum, Sinta