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All Journal BERKALA FISIKA JURNAL SISTEM INFORMASI BISNIS YOUNGSTER PHYSICS JOURNAL Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Journal of Physics and Its Applications Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Information Systems and Informatics Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Fisika Indonesia Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Journal of Applied Data Sciences Jurnal Algoritma Journal of Emerging Science and Engineering Scientific Journal of Informatics
Catur Edi Widodo
Departemen Fisika, Fakultas Sains Dan Matematika, Universitas Diponegoro, Semarang
Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Physics

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Journal : Journal of Applied Data Sciences

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Image-Based Fish Freshness Classification Using Two-Phase Transfer Learning with Deep Learning Fusion Model Helmud, Ellya; Edi Widodo, Catur; Dwi Nurhayati, Oky
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.988

Abstract

This study introduces a novel deep learning approach for automated fish freshness classification using image analysis. The objective is to design and validate a Deep Learning Fusion Model that combines the strengths of EfficientNetB0 and InceptionV3 architectures to improve accuracy and robustness in classifying fresh and non-fresh fish. Input images were subjected to extensive augmentation, including RandomFlip, RandomRotation, RandomZoom, RandomContrast, RandomBrightness, and RandomTranslation, applied exclusively to the training dataset to enhance generalization, followed by backbone-specific pre-processing. Extracted features were fused via global average pooling and forwarded to a newly designed classification head with dropout and L2 regularization to mitigate overfitting. A two-phase transfer learning strategy was employed: initially training the classification head with frozen backbones, followed by fine-tuning the backbone layers using the Adam optimizer with a reduced learning rate. To highlight the contribution of the fusion strategy, ablation studies were conducted with single-backbone models. The EfficientNetB0 model achieved 89.17% validation accuracy, 85.83% test accuracy, and an F1-score of 85.69%, while the InceptionV3 model achieved 86.67% validation accuracy, 81.67% test accuracy, and an F1-score of 81.59%. In contrast, the proposed Fusion Model achieved 93.33% validation accuracy, 95.00% test accuracy, and an F1-score of 94.95%. Additional evaluations with confusion matrices, ROC curves, AUC, and precision-recall curves confirmed the model’s superiority. The findings demonstrate that integrating features from diverse CNN architectures enables the model to learn richer representations, resulting in significantly improved classification performance. The novelty of this work lies in the effective fusion of complementary backbones through global average pooling and fine-tuned transfer learning, establishing a human-centric computational approach that offers a reliable solution for practical fish freshness assessment in food safety and market scenarios.
Optimizing Monkeypox Detection Using Advanced Class Imbalance Handling Methods: Smote, Smote-Enn, Smote-Tomek, Borderline-Smote Rizki, Fahlul; Widowati, Widowati; Widodo, Catur Edi
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1286

Abstract

Monkeypox is a zoonotic viral disease with increasing global concern due to its rapid spread and potential public health impact. Accurate and timely detection is crucial, yet the development of machine learning-based detection systems is often challenged by class imbalance in clinical datasets, leading to biased predictions towards majority classes. This study systematically evaluates the effectiveness of various class imbalance handling techniques, including SMOTE, Borderline-SMOTE, SMOTE-ENN, and SMOTE-Tomek, on the performance of ensemble learning algorithms, specifically Random Forest and Gradient Boosting, for monkeypox detection. Using a dataset of 25,000 synthetic patient records with 11 clinical features, models were trained and validated through stratified 5-fold cross-validation. Performance metrics including accuracy, precision, recall, F1-score, and Area Under the Curve (AUC), along with ROC analysis, were employed to assess the impact of each augmentation method. Results indicate that hybrid methods, particularly SMOTE-ENN, significantly improve recall and F1-score, improving the detection of clinically important monkeypox-positive cases while maintaining adequate discriminative ability. Standard SMOTE and SMOTE-Tomek provide stable performance across metrics, whereas Borderline-SMOTE shows lower recall despite high precision. These findings highlight the importance of selecting appropriate class imbalance handling strategies tailored to the clinical objective, emphasizing sensitivity in detecting positive monkeypox cases. The study provides practical guidance for implementing reliable and robust machine learning models in early monkeypox detection, contributing to improved clinical decision-making and public health interventions.
Co-Authors Agusta Rakhmat Taufani Agustiyar Agustiyar Aprilia Ayu Andarinny Arif Setiawan Aris Puji Widodo Aryasa, Jiyestha Aji Dharma Budi Warsito Choirul Anam AM Diponegoro Dinar Mutiara Kusumo Nugraheni eka rahmawati Eko Sediyono ellya helmud, ellya Eriya, Eriya Faisal Akbar Fatkhur Rohman Figur Humani Fitra Nur Asri, Muh Fitro, Achmad H Hadiyanto Hadiyanto Hadiyanto Hadyan Arifianto Husen Nasrullah Armin I Gusti Bagus Wiksuana Idrus, Nurul Asyikin Irwan Agus Saputro Isnain Gunadi Iswanti, Arie Jatmiko Endro Suseno K. Sofjan Firdausi Koesuma, Sorja Kusworo Adi Kuworo Adi Labib Mustofa, Refo Labib Mustofa, Tarno Maratullatifah, Yulaikha Muchammad Azam, Muchammad Mustafid Mustafid Nanik Susanti Noor Azizah Novettralita, Ucky Pradestha Oky Dwi Nurhayati Oky Dwi Nurhayati Otong Saeful Bachri Paramesvari, Dhenok Prastyaningtyas R. Rizal Isnanto Rachmat Gernowo Rahmatika, Yuni Ramanda, Febri Retno Hapsari, Angga Ayu Rizki, Fahlul Rizky Ayomi Syifa Romadhon, Zainur Royani, Noorhanida Saitem Saitem Salsabila Naqiyah Siswahyudianto Soesanto, Qidir Maulana Binu Sri Mulyani Tri Retnaningsih Soeprobowati Tutur Urip Wahyu Adi Wibowo Wibowo, Mochamad Agung widowati widowati widya astuti Zainal Bachrudin