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All Journal Indonesian Journal of Electrical Engineering and Computer Science Engineering, Mathematics and Computer Science Journal (EMACS)
Riftiarrasyid, Mohammad Faisal
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Monitoring water quality parameters impacted by Indonesia’s weather using internet of things Riftiarrasyid, Mohammad Faisal; Soewito, Benfano
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1426-1436

Abstract

Increasing need for food resources, State of Indonesia to strive to maximize the output of food production. Not only in agriculture but also aquaculture results are also trying to be improved. This is also supported by the increase of Indonesia’s national fish consumption rate from 50.69 Kg per capita in 2018 to 55.37 Kg per capita in 2021. Recent aquaculture research only explored topics about monitoring the cultivation environment. But there have been no studies exploring how bad the impact of weather on the process of farming. Hence, this study aims to measure the influence of weather on freshwater aquaculture pond water quality and analyze its impact on fish growth namely Oreochromis Sp., using pH sensors and dissolved oxygen (DO). Then a weather simulation was carried out based on Indonesia’s tropical climate, which majorly consists of sunny and rainy weather. The experimental results indicate the instability of the pH value during the rainy period. DO values tend to decrease at the end of periods of sunny weather. Moreover, fish growth analysis showed that there was a decrease in food conversion ratio (FCR) by 0.956, specific growth rate (SGR) by 2.13% and survival rate (SR) by 5.715% during rainy weather.
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.
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 Benfano Soewito Halim, Rico Novika, Andien Dwi Zahra, Amalia