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INDONESIA
Journal of Information Systems Engineering and Business Intelligence
Published by Universitas Airlangga
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal ini menerima makalah ilmiah dengan fokus pada Rekayasa Sistem Informasi ( Information System Engineering) dan Sistem Bisnis Cerdas (Business Intelligence) Rekayasa Sistem Informasi ( Information System Engineering) adalah Pendekatan multidisiplin terhadap aktifitas yang berkaitan dengan pengembangan dan pengelolaan sistem informasi dalam pencapaian tujuan organisasi. ruang lingkup makalah ilmiah Information Systems Engineering meliputi (namun tidak terbatas): -Pengembangan, pengelolaan, serta pemanfaatan Sistem Informasi. -Tata Kelola Organisasi, -Enterprise Resource Planning, -Enterprise Architecture Planning, -Knowledge Management. Sistem Bisnis Cerdas (Business Intelligence) Mengkaji teknik untuk melakukan transformasi data mentah menjadi informasi yang berguna dalam pengambilan keputusan. mengidentifikasi peluang baru serta mengimplementasikan strategi bisnis berdasarkan informasi yang diolah dari data sehingga menciptakan keunggulan kompetitif. ruang lingkup makalah ilmiah Business Intelligence meliputi (namun tidak terbatas): -Data mining, -Text mining, -Data warehouse, -Online Analytical Processing, -Artificial Intelligence, -Decision Support System.
Arjuna Subject : -
Articles 283 Documents
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A Socio-Technical Perspective on mHealth Application Design in Indonesia: A Synthesis of User, Clinician, and Expert Perspectives Devi Karolita; Indra Fiqi Ripani; Ariesta Lestari; Felicia Sylviana; Angeline Novia Toemon; Misita Anwar
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.428-442

Abstract

Background: The use of mobile health (mHealth) applications (apps) to support healthcare access, administration, and remote consultation has rapidly expanded in Indonesia. However, persistent challenges related to usability, reliability, clinical integration, and trust hinder sustained use of the proposed method. Existing research often adopts a single-perspective approach, which limits the understanding of how user experiences intersect with clinical practice and system-level factors. Objective: This study aims to explain how user-reported experiences, clinical workflow realities, and ecosystem-level constraints interact to shape sustainable mHealth design and implementation in Indonesia. Methods: This study used a qualitative socio-technical grounded theory design that integrated three complementary data sources: large-scale user reviews of four widely used Indonesian mHealth apps, general practitioner interviews, and digital health experts interviews. Based on prior quantitative analysis of user reviews, the review data were re-analyzed qualitatively and synthesized with interview transcripts through open coding, constant comparison, memoing, and socio-technical categorization. Results: mHealth apps were primarily used for administrative tasks, such as booking and payments. However, the perceptions of reliability, simplicity, and trust shaped sustained use. Users reported system instability, complex interfaces, and transactional failures. General practitioners noted misalignment with clinical workflows, workload demands, and organizational barriers. The experts highlighted broader systemic challenges, including fragmented regulation, poor interoperability, and uneven institutional readiness. Conclusion: Sustainable mHealth implementation in Indonesia depends on coordinated readiness across technical, clinical, organizational, and policy domains. This study offers evidence-based recommendations to guide more context-sensitive mHealth design and implementation.   Keywords: mHealth Apps, Socio-Technical Analysis, User Reviews, Clinical Workflows, Recommendation
Smart System for Mushroom Identification and Classification: A Systematic Literature Review Indra Laksmana; Muhammad Ilhamdi Rusydi; Feskaharny Alamsjah
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.489-506

Abstract

Background: Mushrooms play an essential role in ecological balance and significantly contribute to economic activities. However, the close visual resemblance between edible and toxic species continues to cause fatal poisoning incidents worldwide. Conventional identification approaches rely heavily on expert judgment, making them subjective, time-consuming, and unsuitable for large-scale or real-time use. Recent developments in artificial intelligence (AI), particularly deep learning techniques, have created new opportunities for developing automated and reliable identification systems. Objective: This study systematically reviews recent research on intelligent technologies for mushroom identification and classification. This study maps the main research objectives, data types, morphological features, technologies, algorithms, evaluation practices, and remaining challenges, with particular attention to toxicity classification and safety-critical decision support. Methods: A systematic literature review was conducted following the PRISMA 2020 protocol and the PICOC framework. We screened publications indexed in Scopus between 2021 and 2025. From 1,308 initial records, 90 high-quality studies were selected for detailed analysis. Results: The analysis of the selected studies reveals that the majority of research focuses on discriminating between edible and poisonous mushrooms (52 studies), predominantly using visual characteristics related to shape (64 studies) and color (59 studies). Deep learning techniques, especially convolutional neural networks (CNNs) (40 studies) and vision transformers (11 studies), dominate the field and frequently report classification accuracies exceeding 95%. Despite these achievements, several challenges persist, including the difficulty of fine-grained classification among visually similar species, limited dataset availability, and performance degradation in complex natural environments. Conclusion: Although AI-based approaches have considerable potential to support mushroom identification, their reliability is limited by reliance on visual data alone when species are morphologically similar. Future studies should place greater emphasis on lightweight models for field deployment, multimodal sensing, risk-aware evaluation, and Explainable AI (XAI) so that intelligent systems can be used more safely and transparently in real-world contexts.   Keywords: Mushroom Identification, Artificial Intelligence, Deep Learning, Food Safety, Systematic Literature Review
Deep Learning Model for Classification of Bone Marrow Changes in Lumbar Vertebrae (BMCLVB) Using Magnetic Resonance Imaging (MRI) Data Yasir Hussein Shakir; Tiong Sieh Kiong; Chai Phing Chen; Sachin Sharma Ashok Kumar
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.507-519

Abstract

Background: Bone marrow changes in lumbar vertebrae (BMCLVB) are critically linked to lower spinal pain and various spinal disorders, forming a key diagnostic basis. Accurate automated classification using MRI remains a developing area.  Objective:  This study aimed to develop a hybrid CNN-LSTM model for the automatic classification of BMCLVB from multi-sequence MRI scans. Methods: A newly collected annotated dataset consisting of magnetic resonance imaging scans from 134 patients was utilized, incorporating sagittal T1-weighted, T2-weighted, and STIR sequences. The proposed model uses CNN layers to extract the spatial features from each MRI slice and LSTM layers to capture sequential dependencies across consecutive slices. The dataset was divided into training, validation, and testing groups in a ratio of 70%:20%:10%. The experimental results show that the CNN-LSTM model achieves a classification accuracy of 90.29%, with precision, recall, and F1-score all reaching 91.07%. The proposed approach surpasses various state-of-the-art deep learning models, including CNN, VGG16, VGG19, Xception, ResNet variants, DenseNet201, InceptionResNetV2, NASNetMobile, MobileNetV2, and NASNetLarge. Results: The proposed CNN-LSTM model obtained a test classification accuracy of 90.29%. Precision, recall, and F1-score all reached 91.07%. This performance surpassed that of all benchmark models. Conclusion: The outcomes confirm that the hybrid CNN-LSTM model is highly effective for BMCLVB classification and has strong potential as a clinical decision support tool. This study successfully met its objective, showing the advantage of merged spatial and sequential feature learning for this task. Future work should emphasize extending the dataset and further optimizing the model to improve generalizability across diverse populations and imaging protocols.   Keywords: Bone Marrow Changes Lumbar Vertebrae, MRI, Classification, Hybrid CNN-LSTM Model, Medical Image Analysis

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