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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Sinergi Journal of ICT Research and Applications Jurnal Teknologi Elektro InComTech: Jurnal Telekomunikasi dan Komputer Techne : Jurnal Ilmiah Elektroteknika
Regina Lionnie
Universitas Mercu Buana Jakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Journal : Sinergi

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Performance analysis of various types of surface crack detection based on image processing Regina Lionnie; Rizky Citra Ramadhan; Ahmad Syadidu Rosyadi; Muzammil Jusoh; Mudrik Alaydrus
SINERGI Vol 26, No 1 (2022)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2022.1.001

Abstract

Major cracks on a highway or bridge's concrete surface have a massive risk of damages, accompanied by less maintenance, slow detection, and handling; the worst case of the damage is the structure's total collapse, which can produce fatalities. Moreover, Indonesia's climate and geographical location contribute to a higher level of potential damage to the structure. In order to reduce the potential damage, the need for a surface crack detection system arises. This research analysed three different databases (Database A, B, and C) with different surface concrete crack types, such as early thermal contraction, plastic shrinkage, corrosion reinforcement, and non-crack images. The total images from each Database vary from 14 images for Database A, 80 images for Database B, and 4000 images for Database C. The Otsu thresholding and mathematical morphology operations such as opening, closing, dilation, and erosion with pre-processing methods were combined and produced results for each Database with classification using Euclidean distance calculation. The best results for Database A and B were 100% using combination Otsu thresholding with Laplacian operator and Laplacian of Gaussian filter and the same result for a combination of mathematical morphological operations. The best result using Database C, which had more images than Database A and B, was 80,2% using a combination of mathematical morphological operations. 
Human vs machine learning in face recognition: a case study from the travel industry Lionnie, Regina; Hermanto, Vidya
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.021

Abstract

This research was conducted to help answer whether a machine learning simulation can replace the human ability to recognize human faces, especially under challenges under travel industry requirements. The human ability to recognize faces was evaluated using a series of questions in a survey. The questions challenged the human respondents to recognize faces under similar looks, with hair and makeup disguises, only part of the facial area, and under dark lighting conditions. At the same time, a histogram of oriented gradient (HoG) combined with a support vector machine (SVM) was built for machine learning simulations. The machine learning was evaluated using two datasets, i.e., the Extended Yale B (EYB) Face dataset for challenge under dark lighting conditions and The Extended Makeup Face Dataset (EMFD) for challenge using face with makeup disguise. The results showed that machine learning simulation of the face recognition system yielded accuracy as high as 95.4% under dark lighting conditions and 70.8% under facial makeup disguise. On the contrary, only 48% of respondents accurately recognized human faces in dark lighting. The number was increased to 94-96% when the face images were adjusted first with the contrast adjustment method.  However, only 36-37% of respondents accurately recognized human faces under face makeup disguise. 
Co-Authors Ahmad Syadidu Rosyadi Ellisa Agustina Hermanto, Vidya Ivanna Kristianti Timotius Iwan Setyawan Mochamad Miftakhul Huda Mochamad Miftakhul Huda Mudrik Alaydrus Muzammil Jusoh Rizky Citra Ramadhan Trie Maya Kadarina Wahju Sediono Yulian Safarudin