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All Journal Jurnal Ners Indonesian Journal of Electrical Engineering and Computer Science Engineering, Mathematics and Computer Science Journal (EMACS)
Halim, Rico
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Nursing

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Low-resolution image quality enhancement using enhanced super-resolution convolutional network and super-resolution residual network Riftiarrasyid, Mohammad Faisal; Halim, Rico; Novika, Andien Dwi; Zahra, Amalia
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp634-643

Abstract

This research explores the integration of enhanced super-resolution convolutional network (ESPCN) and super-resolution residual network (SRResNet) to enhance image quality captured by low-resolution (LR) cameras and in internet of things (IoT) devices. Focusing on face mask prediction models, the study achieves a substantial improvement, attaining a peak signal-to-noise ratio (PSNR) of 28.5142 dB and an execution time of 0.34704638 seconds. The integration of super-resolution techniques significantly boosts the visual geometry group-16 (VGG16) model’s performance, elevating classification accuracy from 71.30% to 96.30%. These findings highlight the potential of super-resolution in optimizing image quality for low-performance devices and encourage further exploration across diverse applications in image processing and pattern recognition within IoT and beyond.
Antibacterial Activity of the Secondary Metabolite of Fusarium LBSU Isolate from the Cat’s Whiskers Plant (Orthosiphon stamineus) Zega, Sati Agriani Zega; Halim, Rico; Ginting, Astri Natalia; Piska, Finna
Jurnal Ners Vol. 9 No. 3 (2025): JULI 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v9i3.46029

Abstract

The cat's whisker plant is known to contain bioactive metabolites that have potential as therapeutic agents, while its rhizosphere is a habitat for microorganisms that produce secondary metabolites. Fusarium LBSU isolate were isolate from the rhizosphere of cat’s whiskers plant. Secondary metabolites are produced through liquid fermentation, followed by extraction using ethyl acetate. Antibacterial activity was tested using the disk diffusion method against the Escherichia coli and Staphylococcus aureus. The highest antibacterial activity obtained against Escherichia coli 23.565 mm, higher than Staphylococcus aureus which is 15.3 mm. These findings support the further development of secondary metabolites from rhizosphere isolate as an alternative source of environmentally friendly and effective antibacterial agents.
Hybrid CNN-Based Classification of Coffee Bean Roasting Levels Using RGB and GLCM Features Halim, Rico; Riftiarrasyid, Mohammad Faisal
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.13420

Abstract

This study aims to develop a hybrid Convolutional Neural Network (CNN) model for classifying the roasting levels of Coffea arabica beans by integrating RGB color and GLCM texture features. A total of 1,600 high-resolution images were used, consisting of 1,200 training images and 400 testing images, evenly distributed across four roasting levels: Green, Light, Medium, and Dark. Local feature extraction was performed using a sliding window approach to capture fine-grained color and texture information from each image. Three model types were evaluated: a CNN with RGB-only input, a CNN with GLCM-only input, and a hybrid CNN with dual inputs. The hybrid model consistently demonstrated superior performance, achieving a validation accuracy of 99.74%, with minimal misclassification and stable convergence throughout training. Furthermore, six architectural variations of the hybrid model were tested by applying dropout and L2 regularization techniques. The model combining both dropout and L2 regularization achieved the most balanced results in terms of accuracy, generalization, and training stability. This research contributes an effective feature fusion strategy for fine-grained visual classification tasks, particularly in domains where inter-class visual differences are subtle. The proposed approach offers a cost-effective and scalable solution that is well-suited for real-time implementation in small to medium-sized coffee production facilities, and it shows strong potential for broader applications in agricultural product quality assessment.
Co-Authors Ginting, Astri Natalia Novika, Andien Dwi Piska, Finna Riftiarrasyid, Mohammad Faisal Zahra, Amalia Zega, Sati Agriani Zega