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Jumanto
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Journal of Information System Exploration and Research
Published by shm publisher
ISSN : 29641160     EISSN : 29636361     DOI : https://doi.org/10.52465/joiser
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Journal of Information System Exploration and Research (JOISER) (e-ISSN: 2963-6361, p-ISSN: 2964-1160) is a journal that publishes and disseminates scientific research papers on information systems to a wide audience, particularly within the information system society. Articles devoted to discussing any and all aspects of the most recent and noteworthy advancements in the fields of Decision Science, Computer Science, and Computer Science Applications will be considered for publication. Submit your paper now through Online submission ONLY. The JOISER publication period is carried out every six months, namely in January and July. But, authors can submit their work to JOISER at any time throughout the year, as the submission process is continuous. The JOISER has been indexed by Google Scholar, Crossref, Copernicus, and BASE. The Journal of Information Systems Exploration and Research aim publishes articles concerning the design and implementation information system, data models, process models, algorithms, and software for information systems. Subject areas include data management, data mining, machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. We welcome system papers that focus on decision science and machine learning, computer science application, pplication domains, Internet of Things, which present innovative, high-performance, and scalable solutions to data management problems for those domains.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 32 Documents
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Ensemble Learning-based Potato Leaf Disease Classification Using DenseNet201 and MobileNetV2 Ahmad, Burhan; Alamsyah
Journal of Information System Exploration and Research Vol. 4 No. 1 (2026): January 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i1.597

Abstract

Early and late blight are major threats to potato crops and can cause significant losses for farmers. Early disease classification is essential for quick and appropriate treatment. This study proposes an ensemble learning approach by combining DenseNet201 and MobileNetV2 architectures to classify potato leaf diseases from digital images. The dataset used consists of 2,152 potato leaf images and is processed through normalization, augmentation, and image resizing stages. The ensemble model was trained with optimized parameters and evaluated using accuracy, precision, recall, and F1-score. The test results showed an accuracy of 99.56%, with precision, recall, and F1- score values of 99.56% each. Demonstrated improved performance compared to single CNN models on the evaluated dataset, and offers an accurate and efficient solution for disease detection in the agricultural sector.
Ensemble Deep Learning: A State-Of-The-Art Comprehensive Review Samie, Mahmoud Abdel
Journal of Information System Exploration and Research Vol. 4 No. 1 (2026): January 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i1.614

Abstract

Ensemble learning has been a cornerstone of machine learning, providing improved predictive performance and robustness by combining multiple models. However, in the era of deep learning, the landscape of ensemble techniques has rapidly evolved, influenced by advances in neural architectures, training models, and practical application requirements. This review provides a state-of-the-art survey of ensemble deep learning approaches, focusing on recent developments of ensemble methods. We introduce a classification of ensemble strategies based on model diversity, fusion mechanisms, and task alignment, and highlight emerging techniques such as attention-based ensemble fusion, neural architecture search-based ensembles, and large ensembles of language or vision models. The review also examines theoretical foundations, practical tradeoffs, and domain-specific adaptations in some fields. Compiling state-of-the-art benchmarks, we evaluate ensemble performance in terms of accuracy, efficiency, robustness, and interpretability. We also identify key challenges such as scalability, overfitting, and deployment limitations and present open research directions, including ensemble learning for continuous learning, federated learning, and learning from scratch. By connecting key insights with current trends, this review aims to guide researchers and practitioners in designing and implementing ensemble deep learning systems to address the next generation of AI challenges.

Page 4 of 4 | Total Record : 32

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