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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Fitri, Maulatus Shaffira
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Computer Science & IT

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Evaluation of the Accuracy and Efficiency of Deep CNN Architecture in Feature Extraction for Guava Disease Classification Wicaqsana, Shiva Augusta; Luthfiarta, Ardytha; Dwi Mareta, Amalia Putri; Fitri, Maulatus Shaffira
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11655

Abstract

This study analyzes and compares several Deep Convolutional Neural Network (DCNN) architectures to evaluate the balance between classification accuracy and computational efficiency in guava fruit disease detection. A hybrid DCNN–Machine Learning (ML) approach was applied to 3,784 images from the Guava Fruit Disease Dataset using a 10-fold cross-validation scheme and undersampling techniques to address data imbalance. Six DCNN architectures were systematically tested, and the combination of ResNet50 with Artificial Neural Network (ANN) showed the best performance with an accuracy of 0.9979 and an F1-score of 0.9975, surpassing the InceptionV3 baseline (0.9974). In addition to being the most accurate, ResNet50 was also 2.5 times faster in feature extraction than DenseNet201, demonstrating an optimal balance between accuracy and time efficiency. These findings emphasize the importance of analyzing the accuracy-efficiency trade-off in selecting a DCNN architecture and open up opportunities for developing more efficient models for future agricultural image classification applications.
Co-Authors Ardytha Luthfiarta Dwi Mareta, Amalia Putri Wicaqsana, Shiva Augusta