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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
Intelligent Decision Support System Using MOORA Method for Admission Management Sugara, Eka Prasetya Adhy
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

Admission management requires objective and transparent evaluation methods to ensure fairness and efficiency in both educational and healthcare institutions. Traditional selection processes often rely on subjective judgment, leading to bias and inconsistency. This study proposes an Intelligent Decision Support System (IDSS) using the Multi-Objective Optimization by Ratio Analysis (MOORA) method to optimize multi-criteria admission decisions. The system was developed and validated using real admission data from Madrasah Aliyah Negeri 1 Palembang, Indonesia, and designed for adaptability in healthcare contexts such as patient triage or staff recruitment. The MOORA approach was applied to normalize and weight four evaluation criteria, academic performance, written test, religious knowledge test, and interview results yielding objective and transparent rankings. The developed web-based IDSS, implemented using PHP, MySQL, and Apache, processed 340 applicant records in less than two seconds with consistent outcomes matching expert judgment. The findings confirm that mathematical optimization within intelligent frameworks can significantly enhance fairness, transparency, and reproducibility in admission evaluations across domains. This study contributes to the Intelligent Computing and Health Informatics field by demonstrating how MOORA can bridge educational and healthcare decision systems through a unified multi-criteria evaluation model. Future work will explore machine learning based adaptive weighting and fuzzy extensions of MOORA to address uncertainty and improve scalability in broader institutional applications.
UNDERSTANDING CLINICAL WORKFLOW TRANSFORMATION AFTER EHR IMPLEMENTATION ANTWI, SAMUEL
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

AbstractBackground: The digitization of health records through Electronic Health Record (EHR) systems has become a global priority for enhancing clinical efficiency, improving data accuracy, and facilitating care coordination. In Ghana, the Lightwave Health Information Management System (LHIMS) was introduced to address inefficiencies associated with paper-based workflows. Juaben Municipal Hospital, one of the facilities onboarded, offers a compelling case for evaluating the system’s impact on clinical workflow.Objective: This study explored healthcare professionals’ experiences with LHIMS implementation at Juaben Municipal Hospital, focusing on its perceived impact on workflow, documentation, communication, and patient care. It also aimed to identify challenges and opportunities associated with the system and compare pre- and post-EHR workflows.Method: A qualitative design was employed using in-depth interviews with ten healthcare professionals across clinical units, including physicians, nurses, laboratory technicians, and pharmacists. Thematic analysis was conducted to identify patterns related to workflow changes, documentation practices, communication, training, and system usability.Results: LHIMS significantly improved documentation accuracy, reduced time spent on routine tasks, and enhanced interdepartmental communication through real-time messaging and electronic referrals. However, challenges such as unstable power supply, poor internet connectivity, and limited training constrain system effectiveness. Staff also reported reduced patient interaction due to the urgency of digital documentation. Despite these limitations, LHIMS was widely perceived as a successful intervention that streamlined clinical operations and improved data management.Conclusion: The implementation of LHIMS at Juaben Municipal Hospital has modernized clinical workflows by automating manual processes, enhancing documentation quality, and improving communication across departments. Despite its benefits, persistent infrastructure gaps and limited user training hindered full system utilization. To optimize outcomes, future efforts should prioritize reliable infrastructure, continuous training, and user-centered system design. Crucially, digital efficiency must be balanced with patient-centered care to maintain quality clinical interactions.
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

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