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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 57 Documents
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Evaluation of a Semantic Representation-Based Retrieval Model on a Text Dataset Generated from Image Transformation Firmansyah, Muhammad; Marutho, Dhendra; Ilham, Ahmad; Saputra, Irwansyah
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 increasing demand for efficient multimodal information retrieval has driven significant research into bridging visual and textual data. While sophisticated models like CLIP offer state-of-the-art semantic alignment, their substantial computational requirements present challenges for deployment in resource-constrained environments. This study introduces a lightweight retrieval framework that leverages the BLIP image captioning model to transform image data into rich textual descriptions, effectively reframing cross-modal retrieval as a text-to-text task. We systematically evaluated three retrieval models BM25, SBERT, and T5 on caption-transformed MSCOCO and Flickr30K datasets, utilizing both classical metrics (Recall@5, mAP) and semantic-aware metrics (SAR@5, Semantic mAP). Experimental results demonstrate that T5 achieves superior semantic performance (SAR@5 = 0.561, Semantic mAP = 0.524), surpassing SBERT (SAR@5 = 0.524) and outperforming the lexical BM25 baseline (SAR@5 = 0.312). Notably, the proposed BLIP+T5 pipeline attains 88% of CLIP’s semantic accuracy while reducing inference latency by approximately 60% and decreasing GPU memory consumption by over 60%. These findings underscore the potential of caption-based retrieval frameworks as scalable, cost-effective alternatives to computationally intensive multimodal systems, especially in latency-sensitive and resource-limited scenarios. Future work will explore fine-tuning strategies, domain-adapted semantic metrics, and robustness under real-world conditions to further advance retrieval effectiveness.
Utilization of Information Technology in Disaster Nursing: A Systematic Literature Review in Health Administration Damanik, Balqis
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

This article outlines the preparation procedure for submission to the Journal of Intelligent Computing and Health Informatics (JICHI). The template applies to both the initial submission and the final camera-ready manuscript. Adhering to these instructions is crucial when submitting for review. The document utilizes two fonts: Arial and Times New Roman. The abstract must not exceed 250 words, excluding equations, references, or footnotes. The content of the abstract is divided into five key sections: objectives, materials, method, results, and implications for further research. The primary goal is to provide a comprehensive overview of the research, outlining the aims, methodology, findings, and potential areas for future exploration. This concise structure helps reviewers quickly assess the core aspects of the paper. The results presented offer valuable insights into the application of intelligent computing techniques in healthcare, highlighting opportunities for improving clinical workflows and patient outcomes. Further research may focus on enhancing algorithmic precision, exploring new healthcare domains, or addressing existing challenges in the application of these technologies
Study of data mining techniques to classify the life expectancy of patients with chronic hepatitis Sam'an, Muhammad
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

This study examines a hepatitis patient dataset using eleven machine learning (ML) models, including LR, SVM, KNN, DT, RF, XGBoost, LightGBM, GBDT, Cat- Boost, AdaBoost, and Stacking. The dataset is subjected to various analyses, includ- ing correlation analysis, age distribution exploration, class imbalance resolution, and feature importance evaluation using eight methods: Chi-square, DT, RF, XGBoost, LightGBM, GBDT, CatBoost, and AdaBoost. The results of this study indicate that the implementation of the SMOTE method and feature importance analysis improves the performance of ML models. Among the eleven models used, the LR model achieved the highest accuracy, reaching 93.75% before applying SMOTE and increasing to 100% after its implementation. Furthermore, the SMOTE method suc- cessfully addressed the issue of class imbalance in the dataset, as evidenced by the improvement in accuracy of the RF model after applying SMOTE. Overall, this study demonstrates that the use of the SMOTE method and feature importance analysis, particularly with the Chi-square method, plays a crucial role in improving the performance of ML models. SMOTE helps address class imbalance issues, while feature importance analysis assists in selecting relevant features. By combining both approaches, ML models achieve higher and better accuracy in classifying samples from the minority class
Enhancing Intraoral Dental Lesion Localization via Multi-Scale Ensemble Learning Using a Robust Weighted Box Fusion Approach Syarif, Hisyam; Fatichah, Chastine; Yuniarti, Anny; Zeng, Xinyou; Al-Haddad, Abdullah
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; Tsarev, Roman; Pishgar, Elham
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 Nugroho, Hanityo A; AN, Dorojatun; Pribadi, Rubijanto Juni; Raharjo, Samsudi
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 Prawira, Nurul Adi; Firmansyah, Muhammad; Marutho, Dhendra; Ouhab, Achraf; Ilham, Ahmad
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.

Page 6 of 6 | Total Record : 57

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