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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Journal of Community Development Sevanam: Jurnal Pengabdian Masyarakat
Putu Desiana Wulaning Ayu
Jurusan Teknik Elektro, Universitas Udayana
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Nursing Public Health Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

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Comparative Analysis of Augmentation and Filtering Methods in VGG19 and DenseNet121 for Breast Cancer Classification Seneng, I Kadek; Ayu, Putu Desiana Wulaning; Huizen, Roy Rudolf
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4397

Abstract

Breast cancer is one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Mammography plays a crucial role in early detection, yet challenges in manual interpretation have led to the adoption of Convolutional Neural Networks (CNNs) to improve classification accuracy. This study evaluates the performance of Visual Geometry Group (VGG19) and Densely Connected Convolutional Networks (DenseNet121) in mammogram classification. It examines the impact of data augmentation and image enhancement techniques, including Contrast-Limited Adaptive Histogram Equalization (CLAHE), Median Filtering, and Discrete Wavelet Transform (DWT), as well as the influence of varying epochs and learning rates. A novel approach is introduced by assessing data augmentation effectiveness and exploring model adaptations, such as layer incorporation and freezing during training. Classification performance is enhanced through fine-tuning strategies combined with image enhancement techniques, reducing reliance on data augmentation. These findings contribute to medical imaging and computer science by demonstrating how CNN modifications and enhancement methods improve mammogram classification, providing insights for developing robust deep learning-based diagnostic models. The highest performance was achieved using VGG19 with DWT, a learning rate of 0.0001, and 20 epochs, yielding 98.04% accuracy, 98.11% precision, 98% recall, and a 97.99% F1-score. Data augmentation did not consistently enhance results, particularly in clean datasets. Increasing epochs from 10 to 20 improved accuracy, but performance declined at 30 epochs. The confusion matrix showed high accuracy for Benign (100%) and Cancer (99.5%), with more misclassifications in the Normal class (94.5%).
Exploring Ensemble Architectures on Lung X-Ray Multi-Class Image for Classification Using Convolutional Neural Network and Random Forest Nuriansyah, Devin Garmenta; Ayu, Putu Desiana Wulaning; Hostiadi, Dandy Pramana
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5016

Abstract

The lungs are vital organs that play an important role in the respiratory and circulatory systems. Early detection of lung diseases through medical images, especially Chest X-Ray (CXR), is still a challenge due to the limited amount of data and complexity in image interpretation. This research aims to develop an effective image classification approach for lung disease detection by comparing two main methods: direct training using Convolutional Neural Network (CNN) and a hybrid method involving feature extraction from CNN model, feature selection using Chi-Square method, and classification using Random Forest algorithm. To overcome data imbalance and increase variation, data augmentation techniques such as rotation, vertical and horizontal flipping, and zooming are used. Four popular CNN architectures are used in training, namely VGG16, ResNet-50, InceptionV3, and MobileNet. After training, features are extracted and stored in .csv format. Next, feature selection using the Chi-Square method and classification with Random Forest are performed. The experimental results show that direct CNN training achieves high accuracy, with MobileNet reaching the highest performance at 98.83%. However, this approach requires significant computational resources and longer training time. In contrast, the hybrid method offers competitive accuracy with lower computational demands. The findings highlight the potential of combining deep learning and traditional machine learning to create efficient, accurate, and resource-friendly medical image classification systems. This research has significant implications for supporting early diagnosis of lung diseases, reducing diagnostic workload for medical professionals, and enabling the development of deployable AI-assisted healthcare solutions in resource-limited settings.
Co-Authors Civica Moehaimin Dhewanty Dandy Pramana Hostiadi Gede Angga Pradipta, Gede Angga Hilmi, Muhammad Riza I Made Darma Susila, I Made Darma I Wayan Rizky Wijaya Komang Yuli Santika Made Liandana, Made Mahendra, Tubagus Ni Luh Putri Srinadi Ni Putu Eka Apriyanthi Nugroho, I Made Riyan Adi Nuriansyah, Devin Garmenta Pande Putu Gede Putra Pertama Pradita, Agus Hendra Purnama, I Gusti Agung Vony Putu Manik Prihatini Roy Rudolf Huizen Seneng, I Kadek Sukket, Sasiwimol Sutikno Sutikno Yohanes Priyo Atmojo, Yohanes Priyo