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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 5, No 1 (2024): March" : 5 Documents clear
Enhancing Early Diagnosis of Heart Disease: A Comparative Study of K-NN and Naive Bayes Classifiers Using the UCI Heart Disease Dataset Permana, Angga Aditya; Arsanah, Arsanah
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.v5i1.11251

Abstract

Heart disease remains a leading cause of mortality globally, necessitating accurate predictive models for early detection and intervention. This study conducted a detailed comparative analysis of the K-nearest neighbor (KNN) and naive bayes classifiers using the UCI Repository Heart Disease dataset to determine the most effective algorithm for heart disease prediction. Our results demonstrate that the proposed KNN outperforms naive bayes in terms of several key metrics: KNN achieved an accuracy of 91.25%, which surpasses naive bayes' accuracy of 88.75%. Additionally, KNN exhibited superior precision (92%), recall (90%), and an F1 score (91%) compared to naive bayes, which demonstrated precision of 89%, recall of 87%, and an F1 score of 88%. The findings of this study have substantial practical implications for medical data analysis. The high accuracy and reliability of the KNN algorithm make it a valuable tool for healthcare professionals in the early diagnosis of heart disease. Implementing KNN-based predictive models can enhance patient outcomes by timely and accurate detection of heart disease, facilitating early intervention, and reducing the risk of severe cardiac events. Moreover, the user-friendly interface of the proposed system streamlines the classification process, making it accessible for clinical use. Future research should explore the integration of additional machine learning algorithms and ensemble methods to further improve prediction accuracy. Developing real-time prediction systems integrated with electronic health records (EHR) could revolutionize patient monitoring and proactive healthcare management, ultimately contributing to better patient outcomes and more efficient healthcare delivery.
Padang Cuisine Classification using Deep Convolutional Neural Networks and Transfer Learning Sulistya, Elvina; Tyasari, Fanni; Azahra, Anisa Ismi; Munsyarif, Muhammad
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.v5i1.13960

Abstract

Recent advances in artificial intelligence, particularly deep convolutional neural networks (DCNN), have revolutionized image classification tasks across various domains. However, the application of these techniques to culturally specific food classifications, such as Padang cuisine, remains underexplored. This study aimed to develop a robust model for accurately classifying Padang cuisine using a CNN architecture enhanced with Transfer Learning to address the challenge of distinguishing between visually and texturally similar dishes. The model was trained on a dataset comprising approximately 2500 images of nine distinct Padang dishes, including Rendang and Gulai. Images were preprocessed by resizing, normalizing, and augmented through techniques like rotation and zooming, to enhance model generalizability. A pretrained CNN model was fine-tuned using Transfer Learning to leverage the existing knowledge and improve classification accuracy. The enhanced CNN model achieved an overall accuracy of 92% in classifying Padang cuisine, which significantly outperformed traditional models. Despite this, misclassifications were noted in dishes with similar visual features, such as Sate and certain types of Gulai. The results demonstrate the effectiveness of combining CNNs and transfer learning to accurately classify culturally specific dishes. The findings not only advance the field of food image classification but also have practical implications for automated menu management and culinary education, particularly in preserving and promoting culinary heritage. The integration of AI into culinary heritage documentation represents a significant advancement in preserving cultural diversity and enhancement of technological applications in the culinary industry. Future research should explore larger and more diverse datasets to further refine model accuracy and broaden its applicability to other regional cuisines.
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.
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.
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.

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