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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 2 (2024): September" : 5 Documents clear
EfficientNet for Medical Image Classification: Performance vs. Efficiency in Skin Cancer Detection Purbandanu, Muhammad Wigig; Kurniawan, Arif; Yanuarta, Rizky; Munsarif, Muhammad; A. Awoseyi, Ayomikun
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.v5i2.14338

Abstract

This study applies EfficientNetB2, a computationally efficient convolutional neural network (CNN), to improve the accuracy of skin cancer detection using the heterogeneous HAM10000 dataset. Skin cancer classification poses challenges, including overfitting and class imbalance, which we address through data augmentation, class weighting, and SMOTE (Synthetic Minority Over-sampling Technique). Our model achieved accuracy of 86%, precision of 0.87, recall of 0.85, and an AUC of 0.90. These results outperform comparable architectures, such as ResNet50 and GoogleNet, while maintaining lower computational complexity. The proposed model demonstrates high precision in detecting actinic keratoses and basal cell carcinoma, which require timely treatment, but faces difficulties in differentiating melanoma from benign nevi because of their similar visual appearance. This study highlights the potential of EfficientNetB2 for real-world deployment in resource-limited settings, such as mobile health applications and telemedicine platforms. Future research will focus on integrating attention mechanisms and exploring cross-dataset validation to enhance model generalizability and performance.
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.
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.
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.

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