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All Journal Brilliance: Research of Artificial Intelligence Jurnal INFOTEL
Evi Purnamasari
Universitas Indo Global Mandiri, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics Other

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Improving the Accuracy of Concrete Mix Type Recognition with ANN and GLCM Features Based on Image Resolution Gasim Gasim; Rudi Heriansyah; Shinta Puspasari; Muhammad Haviz Irfani; Evi Purnamasari; Indah Permatasari; Samsuryadi Samsuryadi
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1201

Abstract

Concrete is an essential construction material that is often used due to its strength and durability, but its mix type identification often relies on conventional methods that are less efficient and accurate. This research aims to evaluate the effect of image resolution on the accuracy of concrete mix type recognition using Artificial Neural Network (ANN) and Gray-Level Co-Occurrence Matrix (GLCM) features. The method used involves analysing concrete images at various resolutions: 200 x 200, 300 x 300, 400 x 400, 500 x 500, 600 x 600, and 700 x 700 pixels. The experimental results show that higher image resolutions tend to improve recognition accuracy. all types of image sizes using 1,250 training data and 250 test data. Image sizes of 200 x 200 and 300 x 300 pixels give low accuracy of 42% and 45% respectively, while sizes of 400 x 400 and 500 x 500 pixels show an increase in accuracy to 60.5% and 62.5%. The higher resolutions of 600 x 600 and 700 x 700 pixels produced the highest accuracy of 68% and 70%, respectively. These results indicate that larger image resolutions are able to capture more details and characteristics required for more accurate concrete mix type recognition. This research has implications for improving efficiency and consistency in concrete inspection in the construction industry through the use of AI-based image recognition methods.
CNN-Based Identification of Longan Varieties Using Leaf Vein Patterns M. Aditya Yoga Pratama; Herri Setiawan; Evi Purnamasari
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6926

Abstract

Visual classification of longan seedlings remains challenging due to the similarity of characteristics among varieties, particularly in young leaves. This study applies the Convolutional Neural Network (CNN) method to classify five types of longan seedlings—Diamond River, Matalada, Merah, Itoh, and Pingpong—based on leaf vein patterns, which serve as distinctive features. The dataset consists of 1,000 high-resolution images, divided into 900 for training and 100 for testing. The training process includes preprocessing steps such as cropping to focus on vein patterns, resizing to standardize input dimensions, augmentation to enhance data variety, normalization to scale pixel values, and splitting into training and validation sets. Hyperparameter tuning was performed using a grid search, evaluating combinations of learning rate, batch size, and epochs. The best configuration was achieved at the 80th epoch, with a learning rate of 0,0001 and a batch size of 8. The model achieved a validation accuracy of 0,8444 and a loss of 0,3865. During testing, it reached an accuracy of 0,8000, with an average precision of 0,8266, recall of 0,8000, and f1-score of 0,7843. The best performance was observed in the Merah and Matalada classes, while the Diamond class remained challenging due to visual similarities. CNN proved effective for this task, though further improvement is needed for visually similar classes to enhance classification accuracy.
Co-Authors Gasim Gasim Herri Setiawan Indah Permatasari M. Aditya Yoga Pratama Muhammad Haviz Irfani Rudi Heriansyah Samsuryadi Samsuryadi Shinta Puspasari