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Ahmad Ilham
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Journal of Intelligent Computing and Health Informatics (JICHI)
Published by Universitas Muhammadiyah Semarang
ISSN : 27156923     EISSN : 27219186     DOI : https://doi.org/10.26714/jichi
Core Subject : Health, Science, Engineering,
Computer Science & IT Dentistry Electrical & Electronics Engineering Medicine & Pharmacology Public Health
Journal of Intelligent Computing & Health Informatics (JICHI) was printed in March 2020. JICHI is a scientific review journal publishing that focus on exchanging information relating to intelligent computing and health informatics applied in industry, hospitals, government, and universities. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Two types of papers are accepted: (1) A short paper that discusses a single contribution to a specific new trend or a new idea, and; (2) A long paper that provides a survey of a specific research trend using a systematic literature review (SLR) method, as well as a traditional review method. Topics of interest include, but are not limited to: Intelligent Computing Include Machine Learning; Reinforcement Learning; Computer Vision; Image Processing; Scheduling and Optimization; Bio-inspired Algorithms; Business Intelligence; Chaos theory and intelligent control systems; Robotic Intelligent; Multimedia & Application; Web and mobile Intelligence and Big Data, etc.) Health Informatics Include Electronic health record; E-Health Information; Medical Image Processing & Techniques; Data Mining in Healthcare; Bioinformatics & Biostatistics; Mobile applications for patient care; Medical Image Processing & Techniques; Hospital information systems; Document handling systems; Electronic medical record systems; standardization, and systems integration; ICT in health promotion programmes e-health Guidelines and protocols; E-learning & education in healthcare; Telemedicine Software- Portals-Devices & Telehealth; Public health & consumer informatics; Data Mining & Knowledge Discovery in Medicine; ICT for Patient empowerment; ICT for Patient safety; Medical Databanks-Databases & Knowledge Bases; Healthcare Quality assurance; Nursing Informatics; Evaluation & Technology Assessment; Home-based eHealth; Health Management Issues; Health Research; Health Economics Issues; Statistical Method for Computer Medical Decision Support Systems; Medical Informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 6, No 2 (2025): September" : 5 Documents clear
Consistency Preserving MOORA Framework for Robust Educational Admission and Healthcare Triage Eka Prasetya Adhy Sugara; Arsa Ramadhani; Muhammad Rudiansyah
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.15754

Abstract

Effectively distributing scarce resources presents a major challenge for governance in both competitive school admissions and emergency medical triage. The main problem lies in the instability of conventional ranking algorithms, where even small changes in data or the addition of new candidates often lead to rank reversals. This instability undermines the fairness of student admissions and the safety of patient prioritization. To tackle this problem, this study introduces a consistency-preserving Intelligent Decision Support System based on Multi-Objective Optimization by Ratio Analysis (MOORA). Unlike approaches that depend on linear normalization, this framework employs Euclidean vector normalization to successfully separate subjective weights from objective performance values. The proposed model is tested using a high-dimensional dataset of 340 educational applicants and a simulated healthcare triage scenario of similar size. Experimental results show that the framework maintains a ranking consistency correlation above 0.90 with established baselines while achieving a 0.00% rank reversal rate in scenarios with conflicting criteria. These findings confirm that the proposed algorithmic structure provides a mathematically sound and domain-independent logic for critical institutional decision-making.
The Digital Efficiency Paradox: Modeling the Trade-off Between Documentation Speed and Patient Interaction in Infrastructure-Limited EHR Ecosystems Amanda Appiah Acheampong; Samuel Antwi; Josephine Arhin Gordon; Khadijatu Adiss Yusif; Maame Dankwah Tiboah Asare; Richard Peter Yalley; Zainabu Mamley Adams; Abdul-Mumin Musah Bingle; Ramatu Adamu; Muniratu Abdul Razak; Rosemary Abrefa Bermaa; Francisca Tsidih
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.19048

Abstract

The digitization of clinical workflows through Electronic Health Records (EHR) is a global imperative aimed at enhancing data accuracy and care coordination. However, in resource-constrained environments, the transition from paper-based systems to digital platforms often surpasses the readiness of existing infrastructure. While systems such as the Lightwave Health Information Management System (LHIMS) in Ghana offer the promise of increased efficiency, they also introduce critical dependencies on unstable power and internet connectivity. This situation creates a "Digital Efficiency Paradox," wherein the urgency to document data swiftly before a potential power outage inadvertently diminishes the quality of clinician-patient interactions. This study employs a qualitative-driven process modeling approach at Juaben Municipal Hospital (N=10). We utilize formal Business Process Model and Notation (BPMN 2.0) semantics to reconstruct clinical workflows and apply the Control-Flow Complexity (CFC) metric to quantify the cognitive load shift from manual ($W_{\text{pre}}$) to digital ($W_{\text{post}}$) systems. Computational analysis reveals that while LHIMS reduced patient retrieval latency by approximately 96%, it increased structural complexity (CFC) from 3.0 to 14.0, thereby imposing a higher cognitive burden. Crucially, we identified a phenomenon of "Infrastructure-Induced Process Deadlock," where power outages result in total system paralysis ($\mathcal{I}(\tau)=0$), compelling clinicians to resort to risky hybrid workarounds. Paradoxically, the anxiety of potential system failure drives staff to prioritize "screen time" over "care time," creating a tunnel vision effect. The study challenges the "always-online" paradigm in the Global South. We conclude that digital efficiency must be balanced with structural resilience, advocating for an "Offline-First" architecture that decouples clinical documentation from grid instability to preserve the human element of care.
Resource Efficient Semantic Retrieval Pipeline via Generative Captioning and Text-to-Text Transformers for Bridging the Modality Gap Muhammad Firmansyah; Dhendra Marutho; Irwansyah Saputra; Eleni Vogiatzi
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.19240

Abstract

The rapid expansion of multimodal digital content necessitates the development of robust information retrieval systems capable of bridging the semantic gap between visual and textual data. However, contemporary cross- modal models, such as CLIP, impose significant computational demands, rendering them impractical for real-time deployment in resource-limited environments. To address this efficiency challenge, this study introduces a novel lightweight retrieval pipeline that reconceptualizes cross-modal retrieval as a text-to-text task through generative transformation. The proposed methodology employs the Bootstrapped Language-Image Pretraining (BLIP) model to distill visual features into rich textual descriptions, which are subsequently encoded into dense semantic vectors using the T5 transformer architecture. Extensive experiments conducted on the MSCOCO and Flickr30K datasets demonstrate that the proposed pipeline achieves a Semantic Average Recall (SAR@5) of 0.561, significantly surpassing traditional lexical (BM25) and dense (SBERT) baselines. Notably, while the computationally intensive CLIP model retains a slight advantage in absolute accuracy, our approach delivers approximately 90% of CLIP’s semantic performance while enhancing inference throughput by 2.1× and reducing GPU memory consumption by 62%. These findings confirm that generative semantic distillation offers a scalable, cost-effective alternative to end-to-end multimodal systems, particularly for latency-sensitive applications requiring high semantic fidelity.
Bridging the Digital Divide in Disaster Nursing: A Systematic Review of AI and Telehealth Adoption in Low-Resource Settings Balqis Damanik; Syahferi Anwar; Adewoyin Adejoke Osonuga; Mi Jin Lee
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.17329

Abstract

The integration of Artificial Intelligence (AI) and telehealth has significantly transformed disaster response capabil- ities. Nonetheless, a pronounced "digital divide" poses a risk of exacerbating health inequities, particularly in Low- and Middle-Income Countries (LMICs), where disaster vulnerability is most pronounced. Objective: This systematic review seeks to examine the adoption of digital health technologies in disaster nursing, identifying socio-technical barriers and facilitators through the application of the NASSS (Non-adoption, Abandonment, Scale-up, Spread, and Sustainability) framework. Methods: In accordance with PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, PubMed, and CINAHL for articles published between 2020 and 2025. Studies focusing on nursing roles in disaster contexts were included. The quality of the studies was assessed using the Mixed Methods Appraisal Tool (MMAT). Results: A total of 42 studies were synthesized. The review revealed a stark dichotomy: High-Income Countries (HICs) prioritized AI-driven predictive modeling and data privacy, whereas LMICs concentrated on basic connectivity and mHealth solutions. Key barriers in low-resource settings included infrastructural deficits (unstable power/internet), lack of digital literacy among frontline nurses, and unsustainable pilot projects. Conclusion: While digital health holds immense potential, its current implementa- tion is inequitable. To bridge the digital divide, future interventions must prioritize "frugal innovation" resilient, offline-capable technologies designed for resource-constrained environments rather than uncritically importing complex systems from developed nations. Policy frameworks must also address the foundational digital literacy of the nursing workforce.
A Robust Hybrid Cost Sensitive Stacking Ensemble Model for Hepatitis Survival Prediction and Clinical Decision Support Muhammad Sam'an; Farikhin Farikhin
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.17519

Abstract

Chronic hepatitis continues to pose a significant global health challenge, frequently advancing to liver cirrhosis and hepatocellular carcinoma if not managed with precise prognostic interventions. The capacity to accurately predict patient survival is essential for optimizing resource allocation and treatment planning. Although Machine Learning (ML) has shown promise in medical diagnostics, standard algorithms often underperform when applied to hepatitis datasets characterized by severe class imbalance and high dimensionality. Conventional models tend to bias predictions toward the majority class (survival), resulting in a high rate of False Negatives for the minority class (mortality), which is clinically unacceptable. Moreover, single-classifier approaches often lack the generalization capability necessary for robust clinical deployment. To address these deficiencies, this study proposes a Hybrid Cost-Sensitive Stacking Ensemble Model (HCS-SEM). The framework integrates three strategic components: (1) a rigorous Split-First Synthetic Minority Oversampling Technique (SMOTE) protocol to resolve class skewness without data leakage; (2) a Chi-Square feature ranking mechanism to eliminate redundant clinical attributes; and (3) a Two-Tier Stacking Architecture employing Random Forest, SVM, and Gradient Boosting as base learners, optimized by a Logistic Regression meta-learner. Experimental validation on the UCI Hepatitis dataset demonstrates that HCS-SEM significantly outperforms standalone classifiers and traditional ensemble methods. The model achieves superior performance metrics, particularly in Sensitivity and F1-Score, confirmed by the Friedman Rank Test and Nemenyi post-hoc analysis. These findings suggest that the proposed HCS-SEM provides a robust, clinically viable tool for hepatitis prognosis, offering high-precision decision support for medical practitioners managing high-risk patients.

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