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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 4 Documents
 1 
Search results for , issue "Vol 7, No 1 (2026): March" : 4 Documents clear
Enhancing Intraoral Dental Lesion Localization via Multi-Scale Ensemble Learning Using a Robust Weighted Box Fusion Approach Hisyam Syarif; Chastine Fatichah; Anny Yuniarti; Xinyou Zeng; Abdullah Al-Haddad
Journal of Intelligent Computing & Health Informatics Vol 7, No 1 (2026): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v7i1.20127

Abstract

The early detection of dental diseases is essential for preventing severe oral health complications. However, automated lesion detection utilizing intraoral images remains highly challenging due to severe tooth overlap, occlusion, and visually similar anatomical structures. Under these complex conditions, conventional single-stage object detectors frequently produce redundant and inaccurate bounding boxes, which significantly degrades localization precision. To explicitly resolve this problem, this study proposes a robust multi-scale ensemble learning strategy that integrates bounding box predictions from YOLOv5 and YOLOv8 through a Weighted Boxes Fusion (WBF) mechanism. Unlike traditional post-processing techniques such as Non-Maximum Suppression (NMS) and Soft-NMS, the proposed method fuses overlapping bounding boxes by leveraging confidence-weighted spatial aggregation, thereby preserving critical detection information. Extensive experiments were conducted on a publicly validated intraoral image dataset comprising four distinct clinical classes: caries, cavity, cracks, and normal teeth. Quantitative evaluations demonstrate that the proposed WBF ensemble approach substantially outperforms single- model baselines. The integrated model achieves a mean Average Precision (mAP@0.5) of 66.14%, a Precision of 66.47%, and an Intersection over Union (IoU) of 90.83%, representing a massive improvement over the baseline mAP values of approximately 36 to 37%. Furthermore, rigorous statistical testing validates that these performance gains are highly significant (p < 0.05). Ultimately, these findings indicate that the proposed ensemble framework provides a reliable, high-precision solution for intraoral dental lesion localization, offering substantial viability for real-world clinical diagnostic applications.
A Robust and Interpretable Ensemble Learning Framework for Early Mortality Risk Stratification in Heart Failure Agustiyar Agustiyar; Roman Tsarev; Elham Pishgar
Journal of Intelligent Computing & Health Informatics Vol 7, No 1 (2026): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v7i1.20873

Abstract

Heart failure remains a formidable global health challenge, frequently complicated by cardiorenal syndrome, which necessitates early and dynamic mortality risk stratification. Existing clinical scoring systems fail to capture complex nonlinear biomarker interactions, whereas state of the art deep learning models suffer from high computational overhead, algorithmic opacity, and a propensity for severe overfitting on small scale tabular data. To address these critical gaps, this study proposes a computationally efficient and transparent ensemble machine learning framework utilizing Extreme Gradient Boosting (XGBoost) coupled with Shapley Additive Explanations (SHAP). The methodology was rigorously benchmarked against a comprehensive suite of ten distinct machine learning architectures using the publicly validated UCI Heart Failure Clinical Records dataset. Comprehensive evaluations demonstrate that the XGBoost framework is both statistically robust and computationally superior. An ablation study confirmed that synthetic resampling critically maximized diagnostic recall across all evaluated models to prevent fatal misclassifications. Calibrated to an optimal clinical decision threshold (τ = 0.35), the model effectively balanced sensitivity with specificity. Furthermore, five fold cross validation confirmed its supreme generalization stability, achieving a peak mean Area Under the Curve of 0.907 with the lowest algorithmic variance (±0.027) among the ten evaluated models, successfully highlighting the vulnerability of unregularized neural networks and classical algorithms on restricted medical datasets. Game theoretic SHAP analysis biologically validated the framework by decisively isolating elevated serum creatinine and reduced ejection fraction as the primary drivers of mortality. By amalgamating sub four millisecond inference latency with mathematically rigorous clinical interpretability, the proposed framework provides a robust, real time screening tool to optimize proactive interventions in acute cardiovascular care.
High Precision Cascaded Spatio Temporal Deep Inference for Real Time Histamine Risk Prediction: A Health Informatics Approach Hanityo A Nugroho; Dorojatun AN; Rubijanto Juni Pribadi; Samsudi Raharjo
Journal of Intelligent Computing & Health Informatics Vol 7, No 1 (2026): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v7i1.19802

Abstract

Rapid histamine accumulation in tropical fisheries constitutes a substantial public health hazard, particularly via scombroid poisoning, and underscores the need for rigorous, data-driven cold-chain surveillance. Artisanal vessels (≤ 30 GT), however, predominantly depend on ice-based cooling strategies that are thermally unstable and lack real-time diagnostic functionality, thereby failing to sufficiently suppress microbial growth kinetics under ambient conditions that frequently exceed 30°C. To address this gap, we propose a Cascaded Spatio-Temporal Deep Inference Architecture that couples a Convolutional Neural Network (CNN) for spatial feature denoising with a Long Short-Term Memory (LSTM) network for temporal kinetic modeling. This hybrid architecture assimilates high-frequency thermal measurements from an optimized R404A vapor-compression refrigeration system and predicts histamine risk indices under Arrhenius-based kinetic constraints. Field deployment on a 10 GT vessel demonstrated that the system maintained a highly stable storage temperature of -20.1 ± 0.5°C. The proposed model exhibited high predictive accuracy with an R2 of 0.97 and an RMSE of 0.45°C, significantly outperforming a Linear Regression baseline (RMSE = 1.85°C, p < 0.01). Importantly, the system extended the prime-quality shelf life by more than 52 hours while keeping histamine concentrations well below the U.S. FDA limit of 50 mg/kg. Collectively, these findings support a scalable health informatics framework and indicate that AI-driven predictive certification can substantially reduce food safety risks in resource-limited maritime supply chains.
Robust Few Shot Biological Pathology Classification via Optimized Contrastive MobileNetV2: A Transferable Model for Low Resource Medical Imaging Nurul Adi Prawira; Muhammad Firmansyah; Dhendra Marutho; Achraf Ouhab
Journal of Intelligent Computing & Health Informatics Vol 7, No 1 (2026): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v7i1.20179

Abstract

Artificial intelligence has revolutionized computational diagnostics, however deploying reliable intelligent systems in extreme low-resource environments remains a critical structural challenge in health informatics. Conventional deep learning architectures, such as standard Convolutional Neural Networks (CNNs), are inherently data-hungry, making them prone to severe overfitting and catastrophic generalization failures when applied to rare biological pathologies. To overcome this limitation, we propose an Optimized Contrastive MobileNetV2 architecture embedded within a Few-Shot Learning (FSL) framework. By mathematically modifying the latent space representation using a contrastive loss function, the proposed model learns discriminative metric distances rather than relying on massive raw feature memorization. To rigorously validate the algorithm, we utilize a highly constrained dataset comprising merely 120 biological pathogen samples as a cross-domain proxy testbed, accurately simulating the extreme visual complexity and data scarcity typical of rare medical diagnostic scenarios. Extensive episodic evaluations demonstrate that the proposed methodology significantly outperforms conventional baselines. Under a 10-shot learning paradigm, the contrastive architecture achieved a macro-averaged accuracy of 89.2% and an F1-Score of 89.3%, remaining statistically robust against stochastic variations (p < 0.001). Furthermore, the integration of depthwise separable convolutions restricts the model complexity to approximately 3.4 × 10^6 parameters. Crucially, empirical evaluations confirm that this framework occupies merely 13.5 MB of physical storage and achieves an ultra-low inference latency of 12.5 ms per image. Ultimately, this study establishes a highly transferable, computationally efficient algorithmic model ready for seamless integration into intelligent clinical decision support systems and remote edge-computing health architectures.

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