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All Journal Jurnal Teknik Informatika C.I.T. Medicom International Journal of Basic and Applied Science
Simatupang, Christine Debora
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Physics

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Optimizing expert systems: Advanced techniques for enhanced decision-making efficiency Judijanto, Loso; Simatupang, Christine Debora; Doyle, Heckerman
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.860.pp129-142

Abstract

This research aims to develop a unified mathematical formulation to optimize expert systems by integrating advanced techniques in knowledge representation, inference mechanisms, machine learning, and parallel/distributed processing. The primary objective is to enhance decision-making efficiency in expert systems by optimizing the interaction between these components. The research design focuses on building a comprehensive model that combines ontology-based and frame-based knowledge representation, forward and backward chaining inference, neural networks, Bayesian networks, fuzzy logic, and parallel computing. The methodology includes defining efficiency metrics for each component and combining them into a single optimization model. A numerical example was tested using simulated data to evaluate the performance of the proposed system. Key results show that frame-based knowledge representation, forward chaining, and parallel processing contribute significantly to overall system efficiency. The neural network's low loss function and the Bayesian network's high likelihood value confirm the effective integration of machine learning into the expert system. The research concludes that the unified optimization framework significantly improves decision-making efficiency, with a total efficiency score of 23.09. This approach fills a gap in previous studies, which often focus on individual components in isolation, by providing a holistic model that optimizes all aspects of expert systems simultaneously. Future research should focus on real-world implementations and fine-tuning the model to handle dynamic environments and complex decision-making tasks.
A Stochastic modeling framework for ICU resource allocation during health crises Rangkuti, Saddiyah; Simatupang, Christine Debora; Stephane, Laurent Seychelle; Eloise, Darrell
International Journal of Basic and Applied Science Vol. 13 No. 3 (2024): Dec: Optimization and Artificial Intelligence
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i3.644

Abstract

The unprecedented surge in demand for intensive care services during health crises such as the COVID-19 pandemic has revealed critical limitations in existing ICU resource allocation models, which often fail to adapt to uncertain and dynamic conditions. This study aims to develop and evaluate a stochastic modeling framework to optimize ICU resource allocation under crisis scenarios, accounting for probabilistic patient arrivals, fluctuating treatment durations, and constrained multi-resource environments. The framework integrates discrete-event simulation (DES), queueing theory (specifically M/M/c/K models), and stochastic optimization to simulate real-time ICU operations and support decision-making. A Monte Carlo simulation was conducted over a 24-hour period involving 100 replications, where key parameters included a patient arrival rate of 4 patients/hour, 5 ICU beds, and a service time distribution with an average of 6 hours. The results indicate a high blocking probability of 84.3%, ICU bed utilization of 94%, ventilator utilization of 90%, an average patient waiting time of 2.4 hours, and a delay-sensitive mortality rate of 8%. The expected system cost, incorporating waiting time, mortality, and resource inefficiency penalties, totaled 190 units. These findings demonstrate the model’s capability to reveal critical system bottlenecks and support adaptive, ethically grounded allocation policies. The proposed framework provides practical implications for hospital administrators and policymakers by offering a dynamic, evidence-based decision-support tool to improve ICU efficiency and patient outcomes during emergencies.
Co-Authors Doyle, Heckerman Eloise, Darrell Judijanto, Loso Saddiyah Rangkuti Stephane, Laurent Seychelle