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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 301 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
A Hybrid Deep Texture Feature Approach Based on GLCM and ResNet with Autoencoder for Histopathological Breast Cancer Classification Ahmad Fauzi; Parmonangan Rotua Togatorop; Raju Wandira; Faustinus Respati Dwi Febriantoro; Lukmanda Evan Lubis
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 3 (2026): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.3.810-823

Abstract

Background: Breast cancer is one of the largest contributors to cancer cases in the world. Accurate diagnosis and effective treatment can increase the chances of recovery and improve patients’ life expectancy. To address these challenges, various approaches have been developed, including the use of machine learning and deep learning. Despite the promising performance of deep learning approaches, constructing classification models using full deep learning architectures requires substantial computational resources and longer training times compared to traditional machine learning methods. Objective: This study aims to develop both binary and multiclass classification models for breast cancer subtypes by integrating handcrafted texture features with deep features extracted from Residual Network (ResNet). Methods: This study uses the BreakHis dataset, which includes images at 40×, 100×, 200×, and 400× magnifications. The dataset was divided into training and testing sets using an 80:20 ratio. Texture features derived from Gray Level Co-occurrence Matrix (GLCM) were utilized to capture spatial relationships across multiple distances and orientations. In addition, deep features were extracted from augmented images using transfer learning with a pre-trained ResNet50 model. Feature optimization was conducted using an autoencoder to reduce dimensionality and eliminate feature redundancy. Results: Among the four machine learning algorithms evaluated using the GLCM–ResNet features, Support Vector Machine (SVM) and Artificial Neural Network (ANN) consistently outperformed Random Forest and k-Nearest Neighbors (k-NN), confirming their suitability for handling high-dimensional hybrid feature representations. Moreover, the best performance was achieved using SVM at 40× magnification, with an accuracy of 98.43% for binary classification and 94.17% for multiclass classification. Conclusion: A comparative analysis with existing state-of-the-art methods further validates the effectiveness of the proposed approach, as it achieves competitive performance without relying on excessively complex model architectures. Nevertheless, the relatively lower classification performance observed for the lobular carcinoma subtype highlights the inherent difficulty of distinguishing this class. Keywords: Breast Cancer, ResNet, GLCM, Histopathological Images, Classification
Self-Supervised Augmentation Strategies for Enhancing Painting Datasets in Low-Resources Scenarios Akmal Junaidi; Hendra Kurniawan; Muhammad Rezky Adytama; Novita Triyasri; Dede Aprizal
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 3 (2026): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.3.673-687

Abstract

Background: Painting classification is one example of how artificial intelligence (AI) is supporting artistic endeavors more and more, even in the absence of human intervention. Unfortunately, the lack of high-quality labeled data frequently hinders the effectiveness of AI-driven approaches, particularly in environments with limited resources. For deep learning models trained on an artistic set, these results overfitting and poor generalization ability are typical.  Objective: The augmentation of the low-resource painting dataset using a Self-Supervised Learning (SSL) pre-training approach is examined and analyzed in this work, and its effectiveness for painting classification tasks is evaluated.  Methods: The dataset used and collected from the WikiArt repository was carefully selected and contained 4,452 paintings. This dataset is used for the implementation of a quantitative experimental design. The research workflow generally consists of pre-processing, RandAugment integration, pre-trained SSL (SimCLR), and fine-tuning of ResNet18. Five model configurations were compared: ResNet18, ResNet18 + Pre-trained SSL (SimCLR), ResNet18 + RandAugment, ResNet18 + RandAugment + pre-trained SSL (SimCLR), ResNet34 + RandAugment + pre-trained SSL (SimCLR). The evaluation metrics used to measure model performance are accuracy, precision, recall, and F1 score.  Results: The ResNet18 model trained with RandAugment and pre-trained SSL (SimCLR) produced the best classification performance in all experimental scenarios. The accuracy for this configuration was 78.41%. When compared to the standalone ResNet18, Pre-trained SSL (SimCLR), and augmentation configurations, the results show that augmentation diversity effectively supports SSL-based feature representation learning, resulting in improved generalization capability.  Conclusion: To support SSL-based representation learning for the classification of artistic images under conditions of limited data, this study emphasizes enhancing augmentation diversity. The results show that in low-resource painting classification tasks, the integration of ResNet18+ RandAugment + pre-trained SSL (SimCLR) improves generalization capacity and reduces overfitting.  Keywords: RandAugmentation, Pre-trained SSL, Data Augmentation, Classification, Painting, Low-Resource 
Determinants of Trust in Artificial Intelligence in Indonesian Higher Education: A PLS-SEM Analysis Based on the Trustworthy Artificial Intelligence Framework Dwi Yuniarto; A'ang Subiyakto; Aedah Binti Abd. Rahman
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 3 (2026): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.3.615-628

Abstract

Background: Trust in Artificial Intelligence (AI) is essential for adopting AI in higher education. However empirical validation of trust dimensions in Indonesian academia remains limited.   Objective: This study examines the factors influencing academics’ trust in AI in Indonesian higher education using the trustworthy AI framework of the European Commission.  Methods: A quantitative survey of 109 lecturers (response rate: 62.6%) was conducted using purposive sampling. Data were analyzed through partial least squares structural equation modeling (PLS-SEM).  Results: Robustness is the only dimension with a significant positive effect on trust in AI, while fairness, explainability, transparency, and data privacy show no significant influence. These findings indicate that academics prioritize system reliability, stability, and performance over broader ethical dimensions.  Conclusion: Unlike many previous studies conducted in developed countries where fairness, transparency, explainability, and privacy play important roles, this study found that only robustness significantly influences the trust of academics in AI. These findings provide contextual evidence that Trustworthy AI dimensions may operate differently across higher education environments in developing countries.  Keywords: Artificial Intelligence, Trust, Robustness, Transparency, Fairness, Higher Education 
Generative AI and Youth Creativity: An Extended TAM2 Perspective Nguyen Thi Hoi; Bui Quang Truong; Nguyen Quang Trung; Tran Thi Nhung
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 3 (2026): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.3.659-672

Abstract

Background: Generative Artificial Intelligence is increasingly transforming learning, work, and creative activities among young people. However, limited evidence explains how technological, social, behavioral, and individual factors jointly influence creativity in Generative Artificial Intelligence-assisted environments.  Objective: This study examines the determinants of Generative Artificial Intelligence usage and their effects on youth creativity by integrating the Technology Acceptance Model 2 and the Componential Theory of Creativity.  Methods: A quantitative survey was conducted with 400 young users who had prior experience with Generative Artificial Intelligence. Data were analyzed using Cronbach’s Alpha, Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling.  Results: The proposed model demonstrated excellent fit, with a chi-square to degrees-of-freedom ratio of 1.373, Comparative Fit Index of 0.981, Tucker-Lewis Index of 0.978, and Root Mean Square Error of Approximation of 0.031. Social influence and perceived usefulness significantly promoted habitual use, while technology trendiness and innate creativity positively influenced usage purpose. Usage purpose was the strongest predictor of creativity enhancement, followed by habitual use and innate creativity. The model explained 62% of the variance in creativity enhancement.  Conclusion: Generative Artificial Intelligence serves not only as a productivity tool but also as a catalyst for youth creativity. The integration of the Technology Acceptance Model 2 and the Componential Theory of Creativity highlights how technological, social, behavioral, and individual factors jointly contribute to creativity enhancement, with practical implications for education, policy, and technology development.  Keywords: GenAI, Creativity, TAM2, Componential Theory of Creativity, SEM 
Graph-Based Fuzzy Mamdani Model for Money Laundering Risk Assessment in Mining and Coal Transactions Bambang Hadi Purnomo; Agus Buono; Heru Sukoco; Ivan Yustiavandana
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 3 (2026): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.3.596-614

Abstract

Background: Financial transactions in the mineral and coal mining sector will become increasingly complex, creating the potential for difficult-to-detect money laundering practices. Therefore, methods are needed to identify the reasonableness of the transaction relationship between the sender and recipient of funds.  Objective: This research aims to design and implement a graph-based fuzzy Mamdani model to assess the risk level of transaction relationships that indicate money laundering in the mineral and coal mining sector.  Methods: The research employed a quantitative modelling approach. The dataset consists of financial transaction reports obtained from the Indonesian Financial Transaction Reports and Analysis Center (INTRACT). The graph-based Mamdani Fuzzy Inference System (FIS) is applied to assess the risk level of transaction relationships. The model is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²).  Results: The results show FIS Mamdani model achieved superior performance with an MSE of 0.07, RMSE of 0.26, and R² of 0.93. Testing results with new data whose risk levels are known based on expert assessments and court decisions, show that all risk categories generated by the model are fully aligned with the classifications established based on professional evaluations.  Conclusion: These findings confirm that the graph-based Mamdani fuzzy model is an adaptive and accurate tool for assessing transaction relationship risk, which can be used to detect money laundering in the mineral and coal mining sector. This research contributes to the advancement of graph-based financial transaction monitoring systems and expert systems, enhancing their ability to handle complex and dynamic transaction relationship risks. Further research is recommended to explore integration with advanced machine learning techniques such as deep learning.  Keywords: Graph-Based Model, Mamdani Fuzzy, Money Laundering Risk, Mining Transactions, Artificial Intelligence 
Zero-Shot Per-Criteria Prompting for Systematic Review Acceleration: A Case Study of Title and Abstract Filtering Process Chandra Lesmana; Muhammad Okky Ibrohim; Indra Budi; Meganingrum Arista Jiwanggi
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 3 (2026): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.3.760-774

Abstract

Background: Systematic Literature Review (SLR) is a widely adopted method for obtaining a comprehensive understanding of a specific research area. It involves a structured, transparent, and research question-driven process consisting of three main stages: planning, conducting, and reporting. Although SLR provides objectivity and accuracy, the document selection phase remains labor-intensive and time-consuming due to the need to manually screen large volumes of literature. Objective: This study investigates the potential of large language models (LLMs) to automate the document selection phase in the SLR process by proposing a per-criteria prompting approach that naturally mimics the human annotation process, thereby improving screening efficiency and accuracy. Methods: We propose a per-criteria prompting approach to classify paper metadata using LLM in the zero-shot paradigm. This is done by classifying whether the given paper metadata (title and abstract) is relevant to each inclusion exclusion criteria and then aggregating them. As the baseline, we compare our proposed method to the existing global prompting approach, which directly classifies whether the given paper metadata is relevant to all inclusion exclusion criteria in single steps. Results: The proposed per-criteria prompting approach generally outperforms the global prompting approach. Especially when applied using the Llama model, our proposed approach outperforms the existing global prompting approach in all 10 dataset benchmarks we use. Conclusion: The finding suggests that prompting each criterion significantly boosts the LLM performance because it makes the LLM more focused in classifying the paper metadata into the given inclusion and exclusion criteria. Keywords: Systematic Literature Review, Text Classification, Large Language Model, Per-Criteria Prompting, Zero-Shot Prompting
Conceptual Model of Intelligent Spatial Decision Support System for Evaluating Paddy Land Suitability Riyan Naufal Hays; Marimin Marimin; Yeni Herdiyeni; Widiatmaka Widiatmaka
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 3 (2026): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.3.645-658

Abstract

Background: Paddy is a staple food that is essential for global food security. Paddy land suitability evaluation is key to optimizing land resource management and productivity. Existing land evaluation techniques are manual and rigid, making them less representative of land physical data uncertainty and expert judgment reluctance. Most recent spatial decision support system and fuzzy logic-based techniques use one-dimensional models (membership) without considering expert hesitation.  Objective: This study aimed to develop a conceptual model for an Intelligent Spatial Decision Support System (ISDSS) for evaluating the suitability of paddy by integrating contextual user requirements with Soft System Methodology (SSM) and numerical examples.  Methods: This research methodology integrates SSM to identify problems and numerical examples based on Spherical Fuzzy Set (SFS) for land suitability assessment. The criteria weights are determined using the Spherical Fuzzy Analytical Hierarchy Process (SF-AHP), which represents each criterion through membership (μ), non-membership (ν), and hesitancy (π) degrees to capture hesitation in expert judgment. The findings of the weighing and evaluation were combined using Weighted Overlay Analysis (WOA) to produce a land suitability index.  Results: The SSM approach organizes complex and unstructured problems into a systematic conceptual framework. An example of an SFS calculation demonstrates computational feasibility based on expert validation, yielding an overall accuracy of 90% and a Cohen’s kappa coefficient of 0.861, indicating promising conceptual agreement.  Conclusion: The integration of SSM and numerical examples provides a conceptual foundation for developing ISDSS for evaluating paddy land suitability. Further research will focus on implementing a prototype to test spatial performance and the validity of results at the operational level.  Keywords: ISDSS, Numerical Example, Paddy Land Suitability, Spherical Fuzzy Set, SSM 

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