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All Journal Jurnal Teknik Informatika C.I.T. Medicom Journal of Computer Science and Research
Marsoit, Patrisia Teresa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science

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Development of quantum neural networks for complex data classification Savvas, Asgari; Lizarralde, Mian Snell; Marsoit, Patrisia Teresa
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 4 (2023): Oct: Computing Quantum and Related Fields
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v1i4.32

Abstract

This research explores the development of Quantum Neural Networks (QNNs) as a transformative approach for complex data classification. Utilizing a numerical example, we illustrate the foundational quantum principles of superposition and entanglement within QNNs. The hybrid quantum-classical processing paradigm is introduced, emphasizing the seamless integration of quantum and classical components, acknowledging the challenges of quantum error correction and noise in Noisy Intermediate-Scale Quantum (NISQ) devices. While the example is deliberately simple, it serves as a starting point for understanding the unique advantages and challenges associated with QNNs. Our findings highlight the potential of quantum computation for parallel processing but also underscore the need to address current limitations for practical applications. Future research directions include investigating sophisticated quantum circuits, exploring error mitigation strategies, and assessing QNN performance across diverse datasets. Collaboration between quantum computing and machine learning communities is essential for the advancement of QNNs, and developments in quantum hardware will play a pivotal role in realizing their full potential. This study contributes to the evolving discourse at the intersection of quantum computing and machine learning, providing foundational insights and laying the groundwork for further exploration in this rapidly advancing field.
Leveraging AI for optimization in supply chain decision support: Enhancing predictive accuracy Judijanto, Loso; Riandari, Fristi; Marsoit, Patrisia Teresa
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.Vol16.2024.858.pp171-184

Abstract

This research explores the use of AI-driven techniques to optimize supply chain decision-making by integrating demand forecasting, inventory management, and logistics optimization. The main objective is to enhance predictive accuracy while minimizing overall supply chain costs through the application of machine learning and reinforcement learning methods. The research design involves the development of a comprehensive mathematical model that combines AI-based demand forecasting with cost optimization in inventory and transportation. A machine learning model is employed to predict demand, while optimization techniques are used to minimize inventory and logistics costs. Reinforcement learning is introduced as a method for real-time decision-making, allowing the system to continuously adapt and improve. The methodology involves testing the model through a numerical example, where predicted demand is used to optimize inventory and logistics costs. The main results show that the AI-based model achieves a demand forecasting accuracy with a Mean Squared Error (MSE) of 50, resulting in a total supply chain cost of 760 units, which includes both inventory and transportation costs. Despite the initial prediction error, the model demonstrates the potential for cost savings and operational efficiency through better alignment of supply chain components. The research concludes that while the AI-driven approach offers significant improvements in supply chain management, further refinement of the predictive model and the practical application of reinforcement learning are necessary to fully realize its benefits. Future research should focus on enhancing model accuracy and scalability in real-world supply chain environments
Co-Authors Fristi Riandari Judijanto, Loso Lizarralde, Mian Snell Savvas, Asgari