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All Journal JUTEI (Jurnal Terapan Teknologi Informasi) Jurnal ULTIMA Computing TESLA: Jurnal Teknik Elektro Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Jurnal Bakti Masyarakat Indonesia Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Sosial dan Teknologi Green Intelligent Systems and Applications Journal of Artificial Intelligence and Digital Business Jurnal Serina Abdimas
Meirista Wulandari, Meirista
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : jurnal ultima computing

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Microscopic Sand Image Classification Using Convolutional Neural Networks Redja, Christie; Pranoto, Wati Asriningsih; Wulandari, Meirista
Ultima Computing : Jurnal Sistem Komputer Vol 16 No 2 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i2.3907

Abstract

Abstract— This research paper reviews the use of Convolutional Neural Networks (CNNs) to categorize diverse sand type using microscopic images, with an objective of improving quality control in construction materials. The paper compares three CNN architectures—LeNet-5, Inception v3, and ResNet50—for discriminating between specific sand categories, such as two river sands (Cipongkor and Citarum) and three types of silica sand (brown, cream, and white). Each model was trained and tested on different dataset splits, with images pre-processed to highlight specific microscopic properties. To achieve a thorough comparison, each model's performance was measured using a variety of measures such as F1-score, accuracy, recall, and precision. These measurements enabled a comprehensive evaluation of how accurately and reliably each CNN model categorized the various sand types. ResNet50 consistently delivered the highest accuracy, achieving perfect classification in some instances, showcasing its effectiveness in capturing fine details in sand textures. These results highlight the potential of CNN-based approaches for precise and automated sand classification, which supports increased quality assurance in construction and related areas. Index Terms— Convolutional Neural Network (CNN); sand classification; LeNet-5; Inception v3; ResNet50
Microscopic Sand Image Classification Using Convolutional Neural Networks Redja, Christie; Pranoto, Wati Asriningsih; Wulandari, Meirista
ULTIMA Computing Vol 16 No 2 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i2.3907

Abstract

Abstract” This research paper reviews the use of Convolutional Neural Networks (CNNs) to categorize diverse sand type using microscopic images, with an objective of improving quality control in construction materials. The paper compares three CNN architectures”LeNet-5, Inception v3, and ResNet50”for discriminating between specific sand categories, such as two river sands (Cipongkor and Citarum) and three types of silica sand (brown, cream, and white). Each model was trained and tested on different dataset splits, with images pre-processed to highlight specific microscopic properties. To achieve a thorough comparison, each model's performance was measured using a variety of measures such as F1-score, accuracy, recall, and precision. These measurements enabled a comprehensive evaluation of how accurately and reliably each CNN model categorized the various sand types. ResNet50 consistently delivered the highest accuracy, achieving perfect classification in some instances, showcasing its effectiveness in capturing fine details in sand textures. These results highlight the potential of CNN-based approaches for precise and automated sand classification, which supports increased quality assurance in construction and related areas. Index Terms” Convolutional Neural Network (CNN); sand classification; LeNet-5; Inception v3; ResNet50
Convolutional Neural Network Roasted Coffee Bean Classification Based on Color Hanes, Natanael; Wulandari, Meirista; Setyaningsih, Endah
ULTIMA Computing Vol 17 No 2 (2025): Ultima Computing: Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v17i2.4351

Abstract

Coffee quality is significantly influenced by the roasting level, which is commonly determined by the color of the beans. Traditional classification methods rely on manual sorting and human judgment, making the process labor-intensive, subjective, and prone to error. To address these limitations, this project proposes a deep learning-based coffee bean classification system using Convolutional Neural Networks (CNNs). CNNs, known for their strong performance in image recognition, can analyze visual features like color, texture, and shape to automatically classify coffee beans based on roast level. The system is evaluated using metrics such as accuracy, precision, recall, and F1 score. Among the tested input sizes, the CNN model performs best at 64×64 pixels, achieving a peak accuracy of 99% with minimal misclassifications. This result highlights the model’s effectiveness in delivering high classification performance while maintaining computational efficiency, even with low-resolution images.
Co-Authors Adrianto, Christopher Calvin Alfarabi, Azis Dzaffin Bagus Mulyawan Beatrix, Maria Elbert, Elbert Endah Setyaningsih Fat, Joni Fawzi, Ahmad Hanes, Natanael Hardi, Cindy Aurelia Hendisutio, Andrew Hugeng Julius Sembiring Kelvin Kelvin Pannavudho, Dede Dwi Pranata Pranoto, Wati Asriningsih Redja, Christie Suraidi Wahidin Wahab