Article Per Year (5 Year)
p-Index From 2021 - 2026
7.115
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ComTech: Computer, Mathematics and Engineering Applications Jurnal Mantik Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) INFOKUM Journal of Computer Networks, Architecture and High Performance Computing International Journal of Basic and Applied Science Jurnal Media Informatika Journal Basic Science and Technology Jurnal Mandiri IT CEBONG Journal INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH LEBAH Indonesia Accounting Research Journal Journal of Computer Science and Research International Journal of Enterprise Modelling Journal of Defense Technology and Engineering
Hengki Tamando Sihotang
Universitas Putra Abadi Langkat
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 84 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 84 Documents
Search

 5   6   7   8   9 
Artificial Intelligence Based Multilevel Optimization Models for Complex Decision Systems Hengki Tamando Sihotang; Wildan Alrasyid
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 6 (2026): Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Complex decision systems, such as supply chains, smart cities, and healthcare networks, are characterized by hierarchical structures, dynamic environments, and high levels of uncertainty, making them difficult to optimize using traditional methods. Conventional optimization approaches, which are typically static and single-level, are limited in their ability to handle interdependent decisions and rapidly changing conditions. This study proposes an Artificial Intelligence-based multilevel optimization model to address these challenges by integrating hierarchical optimization with advanced AI techniques. The proposed framework combines multilevel optimization encompassing strategic, tactical, and operational decision layers with Artificial Intelligence methods, including neural networks for prediction, reinforcement learning for adaptive decision-making, and genetic algorithms for global optimization. A simulation-based methodology is employed to model complex environments and evaluate system performance under various scenarios. The results demonstrate that the proposed model significantly outperforms traditional optimization approaches. It achieves higher accuracy, faster convergence, and greater adaptability in dynamic and uncertain environments. Sensitivity analysis confirms the robustness of the model under varying conditions, while scalability tests indicate its effectiveness in handling large-scale systems. These findings highlight the advantages of integrating AI with multilevel optimization for complex decision-making. It offers both theoretical and practical implications for improving decision-making in complex systems. Future research is recommended to enhance computational efficiency, improve model interpretability, and validate the framework through real-world applications across various domains.
A Unified Artificial Intelligence and Stochastic Optimization Framework for Decision-Making in Highly Complex Systems Hengki Tamando Sihotang; Galih Prakoso Rizky A
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Decision-making in highly complex systems is increasingly challenged by uncertainty, dynamic environments, and the availability of large-scale, high-dimensional data. Traditional optimization methods often lack adaptability, while standalone Artificial Intelligence models struggle to explicitly handle uncertainty in a principled manner. To address these limitations, this research proposes a unified framework that integrates Artificial Intelligence with Stochastic Optimization for enhanced decision-making in complex and uncertain environments. The proposed framework combines data-driven learning and probabilistic optimization within a closed-loop architecture consisting of data input, AI-based prediction, stochastic decision-making, and continuous feedback. Advanced AI models, including deep learning and reinforcement learning, are employed to extract patterns and generate predictive insights from real-time and historical data. These outputs are then incorporated into stochastic optimization models, which evaluate decisions under uncertainty using probabilistic constraints and scenario-based analysis. The framework is further strengthened by an adaptive feedback mechanism that continuously updates both learning and optimization components. Experimental evaluation demonstrates that the proposed approach outperforms traditional optimization and pure AI models in terms of decision accuracy, robustness under uncertainty, and adaptability to dynamic environments. The framework also shows improved stability and computational efficiency when applied to large-scale systems. Practical applications in domains such as finance, logistics, and smart city management highlight its real-world relevance. Overall, this research contributes to decision science by bridging the gap between learning and uncertainty modeling, providing a scalable and integrated solution for intelligent decision-making in highly complex systems.
Toward an integrated AI-Driven governance architecture for smart cities and digital economy systems Yulistiawan, Bambang Saras; Hapsanto, Henry Eko; Wibowo, Satriyo; Sihotang, Hengki Tamando
Indonesia Accounting Research Journal Vol. 13 No. 3 (2026): March: IT Governance, Finance, Accounting, Management
Publisher : Institute of Accounting Research and Novation (IARN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/iacrj.v13i3.665

Abstract

The rapid growth of smart city technologies and digital economy systems has significantly increased the complexity of urban governance, particularly in integrating heterogeneous data sources, supporting intelligent decision-making, and ensuring effective coordination across systems. However, existing approaches often remain fragmented, with limited integration between data infrastructures, artificial intelligence (AI), and governance mechanisms. This study addresses this gap by proposing and evaluating an AI-driven governance architecture designed to integrate smart city systems and digital economy ecosystems into a unified, data-driven framework. This research adopts the Design Science Research (DSR) methodology, encompassing problem identification, objective definition, architecture design, demonstration, evaluation, and communication. The proposed architecture is structured into five interconnected layers: data acquisition, data management, AI intelligence, governance, and service delivery. A demonstration scenario integrating smart mobility and digital economy systems illustrates the operational capabilities of the architecture. The evaluation is conducted using a multi-framework approach, incorporating COBIT, ISO 37120, TOGAF, NIST AI Risk Management Framework, ITIL, and GDPR, combined with expert-based assessment. The results indicate that the proposed architecture achieves a high level of effectiveness, with an overall evaluation score of 4.39, demonstrating strong alignment with governance, architectural, and service requirements. This study contributes by introducing an integrated AI-driven governance model that bridges smart city systems and digital economy ecosystems, enabling adaptive, predictive, and data-driven urban governance. The findings provide both theoretical insights and practical guidance for developing next-generation governance architectures in complex digital environments.
Dynamic portfolio optimization using differential evolution: a Markowitz modern portfolio theory approach Hengki Tamando Sihotang; Jonson Manurung; Bambang Saras Yulistiawan; Galih Prakoso Rizky A.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2449-2458

Abstract

An optimal investment portfolio is one of the main focuses in the financial world to minimize risk while maximizing returns. However, the challenge that arises is how to choose the right asset allocation amidst dynamic market uncertainty. This study aims to optimize portfolios based on Markowitz modern portfolio theory (MPT) by using the differential evolution (DE) algorithm as an optimization technique. The data used includes stocks, bonds, and other financial instruments taken from trusted data sources, such as Bloomberg and Yahoo finance, with an observation period of the last five years. The results show that this approach succeeds in finding optimal portfolios with the right asset weights, higher expected returns, and minimized risks compared to conventional approaches. The implication of this research is that the DE algorithm can be effectively used to address portfolio optimization problems in complex and volatile market environments, offering a more adaptive solution for investors to maximize their returns.
Co-Authors A, Galih Prakoso Rizky Achiriani, Tri Wahyuningtiyas Agustina Simangunsong Aisyah Alesha Aisyah Alesha Alrasyid, Wildan Anthoni Anggrawan Anthony Anggrawan Bambang Saras Yulistiawan Bosker Sinaga Budi Arif Dermawan Calvin Berkat Iman Hulu Chandra, Suherman Dadang Pyanto Delano, Aldrich Desi Vinsensia Dini Anggraini Dwiki Rivaldo Naidu Efendi, Syahril Elpridawati Purba Endang Mistaorina Laia Erwin Panggabean Fadiel Rahmad Hidayat Firmansyah Firmansyah Fransisco alexander Simbolon Fristi Riandari Galih Prakoso Rizky A Galih Prakoso Rizky A Galih Prakoso Rizky A. Guntur Syahputra Hapsanto, Henry Eko Harapan Lumbantoruan Harapan Lumbantoruan Harpingka Fitria Br. Sibarani Harpingka Fitriai Br. Sibaran Hasugian , Paska Marto Herlina Zebua Herman Mawengkang Hondor Saragih Husain Husain Hutahaean, Harvei Desmon I Made Aditya Pradhana Putra Jacob, Halburt Jane Irma Sari Jelita Sari Simanungkalit Jijon Raphita Sagala Joan De Mathew Jonhariono Sihotang Jonhariono Sihotang Jonson Manurung Jonson Manurung Judijanto, Loso Kouvelis Geovany Ortizan Laia, Endang Mistaorina Lemos, Sgarbossa Carlo Manurung, Jonson Maria Santauli Siboro Martinus Ndruru Melda Agustina Nababan Michaud, Patrisius Mochamad Wahyudi Muhammad Rafli Muhammad Zarlis Mulianingtyas, RR Octanty Murni Marbun Normi Verawati Marbun Panjaitan, Firta Sari Patricius Michaud Felix Patrisia Teresa Marsoit Pilisman Buulolo Prakoso Rizky A, Galih Pujiastuti, Lise R. Mahdalena Simanjorang Rasenda, Rasenda Rifka Widyastuti Rifka Widyastuti, Rifka Ririn Pebrina Br. Marpaung Rizky A, Galih Prakoso Rizky, Galih Prakoso Rohit Gautama Roma Sinta Simbolon Rosulastri Purba RR Octanty Mulianingtyas Santiwati Sihotang Santoso, Heroe Sethu Ramen Sihotang , Jonhariono Sihotang, Jonhariono Sim, Lee Choi Simbolon, Agata Putri Handayani Simbolon, Roma Sinta Simbolon, Romasinta Siringoringo, Rimmar Siskawati Amri Sitio, Arjon Samuel Song , Jiang Lou Sri Devi Sulindawaty, Sulindawaty Tarisa Tarigan Teresa, Patrys Vina Winda Sari Vinsensia, Desi Wildan Alrasyid Yulistiawan, Bambang Saras