Article Per Year (5 Year)
p-Index From 2020 - 2025
1.152
P-Index
This Author published in this journals
All Journal Jurnal ULTIMA Computing TESLA: Jurnal Teknik Elektro Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Engineering, Mathematics and Computer Science Journal (EMACS) Green Intelligent Systems and Applications
Meirista Wulandari, Meirista
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal ULTIMA Computing

 1 
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
Co-Authors Adrianto, Christopher Calvin Beatrix, Maria Elbert, Elbert Fat, Joni Fawzi, Ahmad Hugeng Kelvin Kelvin Pranoto, Wati Asriningsih Redja, Christie Suraidi Wahidin Wahab