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All Journal EKSAKTA: Journal of Sciences and Data Analysis CAUCHY: Jurnal Matematika Murni dan Aplikasi AdMathEdu : Jurnal Ilmiah Pendidikan Matematika, Ilmu Matematika dan Matematika Terapan Media Farmasi : Jurnal Ilmu Farmasi (Journal Of Pharmaceutical Science) Jurnal Fourier Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Profesi Medika: Jurnal Kedokteran dan Kesehatan JITK (Jurnal Ilmu Pengetahuan dan Komputer) Delta: Jurnal Ilmiah Pendidikan Matematika Indonesian Review of Physics (IRiP) Jurnal Berdaya Mandiri Computer Science and Information Technologies
Sugiyarto Surono
universitas Ahmad Dahlan
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Optimizing EfficientNet for imbalanced medical image classification using grey wolf optimization Khusnul Khotimah; Sugiyarto Surono; Aris Thobirin
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.
The Integration of Hybrid and Game-Based Learning in Physics Education: A Systematic Review of Their Effects on Critical Thinking and Problem-Solving Skills (2018 - 2024) Efi Kurniasari; Sugiyarto Surono; Ishafit Ishafit; Dian Artha Kusumaningtyas; Dwi Sulisworo
Indonesian Review of Physics Vol. 8 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/irip.v8i2.15888

Abstract

This study aims to analyze the impact of integrating hybrid and game-based learning on critical thinking and problem-solving skills in physics education and to identify research trends from 2018 to 2024. The method used was a systematic literature review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The literature search was conducted through Scopus, Web of Science, and Accredited National Journals databases using a combination of relevant keywords. Inclusion criteria included empirical research articles published in English or Indonesian between 2018 and 2024. The selection process involved the stages of identification, screening, eligibility assessment, and inclusion. A total of 16 articles were analyzed further. Data were analyzed using thematic synthesis techniques to identify patterns, methodological trends, and key findings from the reviewed studies. The results indicate that hybrid learning has a more consistent impact on enhancing critical thinking and problem-solving skills, particularly through problem-based approaches and technological support. Meanwhile, game-based learning contributes more to improving motivation, engagement, and understanding of physics concepts. This study contributes a thematic synthesis and comparative analysis of the integration of hybrid and game-based learning and serves as a conceptual foundation for the development of technology-based physics learning models to enhance critical thinking and problem-solving.
Co-Authors Aris Thobirin Attarik Mohammad Aulya Rachma Dewi Dian Artha Kusumaningtyas Ditya Ayu Natalia Endang Darmawan Endang Darmawan Endang Darmawan Endang Darmawan Hariyani Hariyani Ishafit Ishafit Kathina Deswiaqsa Khusnul Khotimah Kurniasari, Efi Nursyiva Irsalinda Qonitat Rohmah Hidayati Rani Nuraeni Salafudin Salafudin Sherlyn Vironica Simeftiany Indrilemta Lomo Suparman suparman Ummu Wachidatul Latifah USWATUN HASANAH Yudi Ari Adi, Yudi Ari Zhurwahayati Putri