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All Journal Ekuitas: Jurnal Pendidikan Ekonomi Indonesian Journal of Data and Science Jurnal Puan Indonesia Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Ekuitas Jurnal Pnedidikan Ekonomi
Libraeni, Luh Gede Bevi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : Indonesian Journal of Data and Science

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Performance Comparison of MobileNet and EfficientNet Architectures in Automatic Classification of Bacterial Colonies Wahyudi, I Putu Alfin Teguh; Sudipa, I Gede Iwan; Libraeni, Luh Gede Bevi; Radhitya, Made Leo; Asana, I Made Dwi Putra
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.218

Abstract

Bacterial colony classification is crucial in microbiology but remains labor-intensive and time-consuming when performed manually. Deep learning, particularly Convolutional Neural Networks (CNNs), enables automated classification, improving accuracy and efficiency. This study compares MobileNetV2 and EfficientNet-B0 for bacterial colony classification, evaluating the impact of data augmentation on model performance. Using the Neurosys AGAR dataset, preprocessing techniques such as histogram equalization, gamma correction, and Gaussian blur were applied, while data augmentation (rotation, noise addition, luminosity adjustments) improved model generalization. The dataset was split (80% training, 20% testing), and models were trained with learning rates (0.0001, 0.001) and epochs (100, 150, 200). Results show EfficientNet-B0 outperforms MobileNetV2, achieving higher validation accuracy and stability, with optimal performance at 150–200 epochs and a lower learning rate (0.0001). Data augmentation significantly improved accuracy and reduced overfitting. While MobileNetV2 remains a lightweight alternative, its performance is heavily reliant on augmentation. These findings highlight EfficientNet-B0 as the superior model, supporting the automation of microbiological diagnostics. Future research should explore hybrid CNN architectures, Vision Transformers (ViTs), and real-time implementation for improved classification efficiency.
Co-Authors Aristamy, I Gusti Ayu Agung Mas Asana, I Made Dwi Putra I Gede Iwan Sudipa Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Putu Eka Kherismawati Purnama, Ni Kadek Liryana Radhitya, Made Leo Rahayu, Ni Made Astini Wahyudi, I Putu Alfin Teguh Willdahlia, Ayu Gede Winatha, Komang Redy