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All Journal Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Creative Information Technology Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi Informasi Cyberku Tedc Jurnal Pengabdian Kesehatan Masyarakat Mulawarman Scientific Journal of Informatics
Guruh Fajar Shidik
Universitas Dian Nuswantoro
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Journal : Scientific Journal of Informatics

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Deep Learning-Based Eye Disorder Classification: A K-Fold Evaluation of EfficientNetB and VGG16 Models Paramita, Cinantya; Rakasiwi, Sindhu; Andono, Pulung Nurtantio; Shidik, Guruh Fajar; Shier Nee Saw; Rafsanjani, Muhammad Ivan
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.26257

Abstract

Purpose: The study evaluates EfficientNetB3 and VGG16 deep learning architectures for image classification, focusing on stability, accuracy, and interpretability. It uses Gradient-weighted Class Activation Mapping to improve transparency and robustness. The research aims to create reliable AI-based diagnostic tools. Methods: The study used a dataset of 4,217 color retinal fundus images divided into four classes: cataract, diabetic retinopathy, glaucoma, and normal. The dataset was divided into 70% for training, 10% for validation, and 20% for testing. The researchers used a transfer learning approach with EfficientNetB3 and VGG16 models, pretrained on ImageNet. Real-time augmentation was applied to prevent overfitting and improve generalization. The models were compiled with the Adam optimizer and trained with categorical cross-entropy loss. Early stopping was implemented to allocate computational resources efficiently and reduce overfitting. A learning rate scheduler (ReduceLROnPlateau) was added to adjust the learning rate if no significant improvement was made concerning validation loss. EfficientNetB3 was more efficient in model size, possessing only 12 million parameters compared to VGG16's 138 million, making it suitable for resource-constrained mobile or embedded systems. The final evaluation was done on the held-out test set. Result: The EfficientNetB3 architecture outperforms VGG16 in classification accuracy and loss value stability, with an average accuracy of 93%. It also exhibits better transparency and predicted accuracy, making it a reliable model for medical image categorization. Novelty: This work introduces a novel framework integrating EfficientNetB3 architecture, stratified cross-valuation, L2 regularization, and Grad-CAM-based interpretability, focusing on openness and explainability in model evaluation.
Optimizing Deep Learning Models with Custom ReLU for Breast Cancer Histopathology Image Classification Nugroho, Wahyu Adi; Supriyanto, Catur; Pujiono, Pujiono; Shidik, Guruh Fajar
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.12722

Abstract

Purpose: The prompt identification of breast cancer is crucial in preventing the considerable damage inflicted by this dangerous form of cancer, which is widely happened across the globe. This study seeks to refine the efficacy of a deep learning-driven approach for the precise diagnosis of breast cancer by employing diverse bespoke Rectified Linear Units (ReLU) to improve the model's performance and reduce inaccuracies within the system. Method: This study focuses on analyzing a deep learning approach utilizing the BreakHis dataset with 7,909 images, incorporating changes to the ReLU activation function across different pre-trained CNN models. It then evaluates performance through measurement such as accuracy, precision, recall, and F1-Score. Result: Based on our experiment results, it can be shown that the DenseNet201 models with a custom LeakyReLU excel beyond the typical ReLU, achieving the highest accuracy, recall, and F1-Score at 99.21%, 99.21%, and 99.11%, respectively. Simultaneously, ResNet152, utilizing LessNegativeReLU (α=0.05), achieved the highest precision at 99.11%. The VGG11 model exhibited the most notable performance enhancement, with improvements ranging from 1.39% to 1.59%. Novelty: The research is original in optimizing a model for accurate breast cancer diagnosis. The proposed model is superior to the model utilizing the default activation function. This finding indicates that the study significantly enhances performance while effectively minimizing errors, thereby necessitating further exploration into the effectiveness of the customized activation function when applied to other medical imaging modalities.
Co-Authors Abdussalam Abdussalam, Abdussalam Affandy Affandy Aisyatul Karima Andrean, Muhammad Niko Andreas Wilson Setiawan Anggraini, Fitria Anhsori, Khusman Astuti, Yani Parti Azzahra, Tarissa Aura Budi Harjo Cahaya Jatmoko Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Chaerul Umam Chaerul Umam Christy Atika Sari Dewi Pergiwati Dliyauddin, Muhammad Doheir, Mohamed Dwi Eko Waluyo Dwi Puji Prabowo, Dwi Puji Dzaky, Azmi Abiyyu Edi Noersasongko Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erlin Dolphina Erna Zuni Astuti Fafaza, Safira Alya Fajrian Nur Adnan Fakhrurrozi Fakhrurrozi, Fakhrurrozi Firmansyah, Rusmal Harun Al Azies Hayu Wikan Kinasih Heru Lestiawan I Ketut Eddy Purnama Ika Pantiawati Islam, Hussain Md Mehedul Junta Zeniarja Kusuma, Edi Jaya Kusumawati, Yupie L. Budi Handoko Lenci Aryani Megantara, Rama Aria Mochamad Hariadi Muhammad Huda, Alam Muhammad Naufal, Muhammad Ningrum, Amanda Prawita Nurmandhani, Ririn Paramita, Cinantya Pergiwati, Dewi Praskatama, Vincentius Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto Putra, Permana Langgeng Wicaksono Ellwid Rafsanjani, Muhammad Ivan Rahadian, Arief Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Rastri Prathivi Ratmana, Danny Oka Ricardus Anggi Pramunendar Riri Damayanti Apnena Rohman, Muhammad Syaifur Saputra, Filmada Ocky Saraswati, Galuh Wilujeng Sarker, Md. Kamruzzaman Savicevic, Anamarija Jurcev Shier Nee Saw Sinaga, Daurat Sindhu Rakasiwi Soeleman, M. Arief Sri Winarno Swanny Trikajanti Widyaatmadja Vincent Suhartono Wahyu Adi Nugroho Wellia Shinta Sari Winarsih, Nurul Anisa Sri Yaacob, Noorayisahbe Mohd Yani Parti Astuti Zainal Arifin Hasibuan Zami, Farrikh Al Zul Azri bin Muhamad Noh