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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) International Journal of Advances in Data and Information Systems Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Matematika
Aris Thobirin, Aris
University of Ahmad Dahlan
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : computer science and information technologies

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Optimizing EfficientNet for imbalanced medical image classification using grey wolf optimization Khotimah, Khusnul; Surono, Sugiyarto; Thobirin, Aris
Computer Science and Information Technologies Vol 6, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i2.p112-121

Abstract

The advancement of deep learning in computer vision has result in substantial progress, particularly in image classification tasks. However, challenges arise when the model is applied to small and unbalanced datasets, such as X-ray data in medical applications. This study aims to improve the classification performance of fracture X-ray images using the EfficientNet architecture optimized with grey wolf optimization (GWO). EfficientNet was chosen for its efficiency in handling small datasets, while GWO was applied to optimize hyperparameters, including learning rate, weight decay, and dropout to improve model accuracy. Random cropping, rotation, flipping, color jittering, and random erasing, were used to expand the diversity of the dataset, and class weighting is applied to overcome class imbalance. The evaluation uses accuracy, precision, recall, and F1-score metrics. The combination of EfficientNetB0 and GWO resulted in an average 4.5% improvement in model performance over baseline methods. This approach provides benefits in developing deep learning methods for medical image classification, especially in dealing with small and imbalanced datasets.
Hybrid feature fusion from multiple CNN models with bayesian-optimized machine learning classifiers Rismawati, Dewi; Surono, Sugiyarto; Thobirin, Aris
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p315-325

Abstract

Information technology advancements have created big data, necessitating efficient techniques to retrieve helpful information. With its capacity to recognize and categorize patterns in data, especially the growing amount of picture data, deep learning is becoming a viable option. This research aims to develop a medical image classification model using chest X-Ray with four classes, namely Covid-19, Pneumonia, Tuberculosis, and Normal. The proposed method combines the advantages of deep learning and machine learning. Three pre-trained CNN models, VGG16, DenseNet201, and InceptionV3, extract features from images. The features generated from each model are fused to enhance the relevant information. Furthermore, principal component analysis (PCA) was applied to reduce the dimensionality of the features, and Bayesian optimization was used to optimize the hyperparameters of the machine learning algorithms support vector machine (SVM), decision tree (DT), and k-nearest neighbors (k-NN). The resulting classification model was evaluated based on accuracy, precision, recall, and F1-score. The results showed that FF-SVM, which is the proposed model, achieved an accuracy of 98.79% with precision, recall, and F1-score of 98.85%, 98.82%, and 98.84%, respectively. In conclusion, fusing feature extraction from multiple CNN models improved the classification accuracy of each machine-learning model. It provided reliable and accurate predictions for lung image diagnosis using chest X-Ray.
Co-Authors Abdillah, Alfiyyah 'Ainul Abdulnazar, Mohamed Naeem Antharathara Andhika, Reynaldi Ikbar Surya Ariani, Nabilah Evi Audina, Maretta Mia Damayanti, Fanita Eko Prasetyo Firdausy, Salma Nadya Goh Khang Wen Hanifa, Riza Aulia Ibad, Irsyadul Ida Ayu Putu Sri Widnyani Khoirun Nisya khusnul khotimah Mujhid, Almuzhidul Parinzka, Zellya Rahmadani, Lanova Ade Rismawati, Dewi Salsabila, Sona Regina Subrata, Arsyad Cahya Sugiyarto Surono, Sugiyarto Suris, Badzliana Aqmar Surono , Sugiyarto Vironica, Sherlyn Wati, Marfungah Wijayanti, Dian Eka