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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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Evaluating User Satisfaction with Hospital Management Information Systems: A PIECES Framework Analysis at Wates General Hospital Ferdiana, Ulya Frista; Pramono, Angga Eko
Journal of Intelligent Computing & Health Informatics Vol 5, No 1 (2024): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i1.13317

Abstract

This study examines the impact of the PIECES framework—Performance, Information, Economics, Control, Efficiency, and Service—on user satisfaction with the Hospital Management Information System (SIMRS) at Wates General Hospital. Despite the hospital's adoption of SIMRS for managing inpatient daily census, issues with data accuracy persist, leading to concerns about system effectiveness and efficiency. Using a cross-sectional design, we collected data from 72 respondents through structured questionnaires and observations. The study employed univariate, bivariate, and multivariate analyses to assess the relationships between the PIECES aspects and user satisfaction. The findings reveal that each PIECES aspect significantly influences user satisfaction, with control and security emerging as dominant factors. Logistic regression analysis indicates that robust control and security measures drastically increase user satisfaction, followed by system performance. Based on these results, we recommend targeted improvements to SIMRS security protocols and system performance to enhance overall user satisfaction. This research contributes to the theoretical understanding of hospital information system evaluation and offers practical recommendations for improving implementation of SIMRS in healthcare settings.
Impact of Information and System Quality on User Satisfaction with Outpatient EMRs at RSKIA Sadewa, Indonesia Putri, Dela Astia; Sutrisno, Trismianto Asmo
Journal of Intelligent Computing & Health Informatics Vol 5, No 2 (2024): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i2.11845

Abstract

The use of electronic medical records (EMR) in outpatient services continues to increase, however, many hospitals still face challenges in ensuring optimal adoption and user satisfaction. Previous research suggests that issues related to information quality and system stability may hinder the effective use of EMRs, but not many studies have specifically analyzed these two factors in the context of regional hospitals. This study aimed to evaluate the effect of information and system quality on EMR user satisfaction in RSKIA Sadewa, Yogyakarta, and identify strategic improvement steps. Primary data were collected from 42 questionnaires distributed to EMR users, and 37 responses were analyzed using multiple linear regression. Results showed that system quality (β = 0.213, p < 0.05) and information quality (β = 0.199, p < 0.05) had a significant influence on user satisfaction, with a joint contribution of 81.1% (R² = 0.811). Although the system provided sufficient features, system reliability constraints and incomplete information negatively affected user experience. System menu optimization and regular training are proposed as strategic measures to improve operational effectiveness. The findings provide important insights for hospital managers and policymakers regarding the importance of strengthening IT infrastructure and data validation to support more effective EMR implementation. Further research is recommended to involve various hospitals in different contexts to expand external validity and provide more comprehensive recommendations for the healthcare sector.
Enhancing Agricultural Pest Detection with EfficientNetV2-L and Grad-CAM: A Comprehensive Approach to Sustainable Farming Agatra, Denaya Ferrari Noval; Cornella, Barisma Ami; Muza'in, Muhammad; Munsarif, Muhammad; Abdollahi, Jafar; Ilham, Ahmad
Journal of Intelligent Computing & Health Informatics Vol 5, No 1 (2024): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i1.13959

Abstract

In modern agriculture, quickly identifying agricultural pests is essential for maintaining high crop yields and ensuring global food security. In diverse and dynamic agricultural environments, traditional pest detection methods exhibit reduced accuracy, limited scalability, and lack interpretability. In this study, EfficientNetV2-L and Grad-CAM were used to significantly enhance pest detection system performance and transparency. EfficientNetV2-L, a fast and resource-efficient model, excels particularly in computationally constrained environments. Traditional CNN models, including EfficientNetV2-L, are criticized as uninterpretable "black boxes" despite their high accuracy. To address this issue, Grad-CAM was used to generate salient maps that visually show the most influential areas of the input image in the model’s decision-making process. This combination not only provides superior pest detection accuracy but also provides actionable insights into the model’s predictions, which is an important feature for building trust among agricultural practitioners. Our experimental results show a 15% improvement in detection accuracy compared to conventional models, especially in identifying visually similar-looking pest species that are often misclassified. In addition, the enhanced interpretability provided by Grad-CAM has led to a deeper understanding of the model’s behaviour, enabling iterative adjustments and improvements that further enhance the reliability of the system. The practical implications of these findings are significant: this integrated model offers a robust solution that can be seamlessly applied to real-time agricultural monitoring systems. With the early detection and proper classification of pests, this model can be used as a more effective pest management strategy to minimize crop damage and increase agricultural productivity. This research not only advances the technological frontier of pest detection but also contributes to broader goals related to sustainable agriculture and food security. Future research will focus on expanding the applicability of this model across different agricultural contexts, improving its adaptability to different environmental conditions, and further optimizing its performance through advanced techniques such as transfer learning and ensemble methods.
NAKNN: An Efficient Classification of Indonesian News Texts with Nazief-Adriani and KNN Ansor, Basirudin; Ramdani, Aditya Putra; Sari, Nova Christina; Al Amin, Muhammad Zainudin; Solichan, Achmad; Mahadewi, Kilala
Journal of Intelligent Computing & Health Informatics Vol 5, No 2 (2024): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i2.15420

Abstract

Internet usage in Indonesia has seen a significant increase, reaching 215.63 million users in 2022-2023, or 78.19% of the population. With the ease of internet access, digital news portals like Narasi TV have become a primary source of information for many people. However, the large number of news articles makes manual categorizing challenging. This study aims to classify Indonesian-language news documents from Narasi TV using the Nazief-Adriani algorithm for stemming and the K-Nearest Neighbor (KNN) method for classification. The text mining process begins with preprocessing, which includes case folding, tokenizing, stop-word filtering, and stemming. Using a dataset of 500 news documents, the study demonstrated that with a 90:10 data split, the average accuracy reached 93%, with the highest value being 100%. For the 80:20 data split, the average accuracy was 89%, with the highest value being 93%, and for a 70:30 data split, the average accuracy was 87%, with the highest value being 89%. In conclusion, the combination of the Nazief-Adriani algorithm and the KNN method with optimal k selection and random states obtained high accuracy, obtaining an average accuracy of 93%) in classifying Indonesian-language news documents. These results demonstrate the significant potential of text mining and classification techniques to manage digital news.
Optimizing Medical Image Security Using Combined DWT-DCT-SVD Watermarking and RLE Compression Strategies Mahiruna, Adiyah; Ngatimin, Ngatimin; Aulia, Lathifatul; Oleiwi, Ahmed Kareem; Rachmawanto, Eko Hari
Journal of Intelligent Computing & Health Informatics Vol 5, No 1 (2024): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i1.14256

Abstract

Medical images, including MRI, CT, ultrasound, X-rays, and ECG, are crucial for diagnostics; however, they present significant data security challenges. This study introduces a novel watermarking technique that utilizes discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) to enhance the security, confidentiality, and integrity of medical images. In addition, Run Length Encoding (RLE) is implemented for efficient compression, which significantly reduces data memory requirements. The proposed method demonstrated a notable improvement in the peak signal-to-Noise Ratio (PSNR), increasing by up to 5 dB compared to existing techniques, and achieved a file size reduction of 15-30%. These advances ensure that high-quality images consume less storage space while maintaining diagnostic integrity. The improved PSNR values indicate that the watermark remains imperceptible, making the proposed method highly effective for clinical applications. Compared to existing methods, the proposed method offers enhanced robustness against digital attacks and better image quality preservation. These findings support the secure and efficient handling of medical image data, thereby promoting their use in clinical environments.
Development of a QR Code-Based Inventory System and Consumable Material Management using Django Python Yaqin, Moh Ainol; Ariyanto, Satria Agus; Amtini, Laila Silvia; Zhalianti, Fina Nuralita
Journal of Intelligent Computing & Health Informatics Vol 5, No 2 (2024): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i2.15578

Abstract

Inventory management and consumable materials in educational institutions, particularly at the Faculty of Health at Nurul Jadid University, face significant challenges. Current manual recording and reporting methods are prone to errors, leading to operational inefficiencies and financial risks, especially given the high cost of medical equipment. In addition, unrecorded inventory data can be lost, and consumable materials can either run out prematurely or accumulate unused. Lack of transparency and accuracy in tracking further complicated planning and decision-making. QR Code technology and the Django Python framework are widely used in other industries; however, their application in education, particularly healthcare, remains underexplored. Unlike previous studies that primarily focused on commercial applications, this study delves into the underexamined area of healthcare inventory management in educational settings, offering a scalable and efficient solution using modern technology. This study addresses these empirical and research gaps by developing a QR Code-based inventory system using Django Python to manage consumable materials at the Faculty of Health. The proposed system enhances efficiency, accuracy, and transparency by providing real-time data for better decision-making. The evaluation will focus on reliability, user-friendliness, ease of access, response time, and user satisfaction. This research not only contributes to the literature on technology applications in education but also provides a practical model for other institutions.
AI-Driven Traffic Simulation using Unity: Implementing Finite State Machines for Adaptive NPC Behaviour Amalia, Syavira; Abidullah, M. Dzawil Fadhol; Marcellino, Fernanditho; Rabani, Diaz Dafa; Azzahra, Firna Fatima; Abdiansah, Abdiansah
Journal of Intelligent Computing & Health Informatics Vol 5, No 2 (2024): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i2.14595

Abstract

This research develops an AI-powered traffic simulation using the Unity Engine, leveraging finite state machines (FSM) to enable adaptive and responsive non-player characters (NPCs). The integration of FSM with advanced pathfinding algorithms, such as A*, allows NPCs to dynamically adjust their behavior based on traffic conditions, obstacles, and environmental changes. The experimental results indicate a 25% improvement in route optimization and a 30% reduction in path conflicts compared to conventional static models, demonstrating the robustness of the proposed approach. Optimized navmesh deployment further enhances navigation fluidity, ensuring efficient agent movement in high-density scenarios without compromising system performance. The findings establish the effectiveness of the FSM-driven NPC behavior in simulating realistic traffic environments, contributing both to the advancement of AI applications in game development and urban planning. By providing an interactive platform for traffic management, this simulation offers a practical tool to study congestion patterns and test intervention strategies. In addition, it improves player engagement by fostering emergent gameplay through dynamic NPC interactions. Future work could explore the integration of real-time procedural generation or multiplayer functionality to enrich simulation depth and scalability. This study provides a comprehensive framework that bridges AI-based mechanics with simulation technology, providing significant insights for researchers and practitioners in game design, artificial intelligence, and urban planning.
Shortest Route Optimization with Genetic Algorithm Implementation in JavaScript. Fitriyadi, Farid
Journal of Intelligent Computing & Health Informatics Vol 6, No 1 (2025): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i1.15777

Abstract

Shortest route optimization is a classic problem in the field of combinatorial optimization with wide applications in various domains, such as transportation, logistics, and path planning. This research aims to solve the shortest route optimization problem using a genetic algorithm implemented in the JavaScript programming language. Genetic algorithms are metaheuristic methods inspired by the principles of biological evolution, such as natural selection, crossover, and mutation. In this study, the representation of individuals in the genetic algorithm is the sequence of cities visited, and the fitness function is based on the total distance traveled. The initial population is randomly generated, and the evolution of the population occurs through a series of generations by applying genetic operators. The experimental results show that after 200 generations, the genetic algorithm successfully finds a route with a total distance of 26.540475042607596, which is close to the optimal solution. The implementation of the genetic algorithm in JavaScript demonstrates its potential as an effective tool for solving the shortest route optimization problem. However, several suggestions for further development are also discussed, including parameter tuning, alternative selection strategies, variations of genetic operators, hybridization with other algorithms, and visualization of results. This research provides insights into the application of genetic algorithms in shortest route optimization and demonstrates how this technique can be implemented using JavaScript as a popular programming language.
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.

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