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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Dan Sistem Informasi Bisnis
Maricar, M Azman
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Computer Science & IT Electrical & Electronics Engineering

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Implementasi Arsitektural Resnet-34 Dalam Klasifikasi Gambar Penyakit Pada Daun Kentang Pranatha, Made Doddy; Maricar, M Azman; Setiawan, Gede Herdian
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 3 (2024): Juli 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i3.1431

Abstract

This research develops and implements an image classification method using the Residual Network (ResNet) architecture to identify potato leaf diseases, achieving an accuracy of around 97%. The dataset used consists of 2152 potato leaf images, categorized into three classes: early blight, late blight, and healthy. The selected model is ResNet-50, known for its ability to address the vanishing gradient problem, allowing for the training of very deep networks. The model training process involves data augmentation to enhance dataset diversity and prevent overfitting. Additionally, hyperparameter optimization was performed to maximize the model's performance. Evaluation of the model shows that ResNet-50 can achieve an accuracy of approximately 97% on the test data, indicating the model's high capability in accurately recognizing and classifying the condition of potato leaves. These results demonstrate the significant potential of using ResNet in plant disease image classification applications, which is crucial for decision-making in agricultural management. This research underscores the importance of deep network architectures and data augmentation techniques in improving the performance of deep learning models.
Utilization of ResNet Architecture and Transfer Learning Method in the Classification of Faces of Individuals with Down Syndrome Pranatha, Made Doddy Adi; Setiawan, Gede Herdian; Maricar, M Azman
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8474

Abstract

Classifying the faces of individuals with Down Syndrome poses a significant challenge in image processing and genetic anomaly detection. This study leverages the ResNet34 architecture and transfer learning methods to improve classification accuracy for Down Syndrome facial recognition. Three experiments were conducted, varying the batch size, learning rate, and number of epochs. In the first experiment, the model achieved an accuracy of 82.83%, precision of 0.8362, recall of 0.8350, and an F1 score of 0.8348, showing promising performance but falling short of the target accuracy of 85%. The second experiment yielded the best results, with an accuracy of 87.88%, precision of 0.8956, recall of 0.8956, and an F1 score of 0.8956, indicating an optimal balance between correct predictions and errors. The third experiment resulted in the lowest accuracy, at 80.47%, with a precision of 0.8272, recall of 0.8249, and an F1 score of 0.8247, signifying a decline in performance compared to the other trials. Among the three experiments, the best configuration was achieved in the second trial, as the high recall value is crucial in medical contexts to ensure that as many individuals with Down Syndrome are correctly detected as possible, minimizing the risk of serious consequences due to false negatives.
Co-Authors Made Doddy Adi Pranatha, Made Doddy Adi Pranatha, Made Doddy Setiawan, Gede Herdian