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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom INFOKUM International Journal of Basic and Applied Science Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) International Journal of Enterprise Modelling Vertex Law and Economics Jurnal Pengabdian Masyarakat Nauli
Jonhariono Sihotang
Universitas Putra Abadi Langkat, Sumatera Utara, Indonesia
Aerospace Engineering 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation

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Graph-based Exploration for Mining and Optimization of Yields (GEMOY Method) Sihotang, Hengki Tamando; Riandari, Fristi; Sihotang , Jonhariono
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 2 (2024): May: 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.Vol16.2024.777.pp70-81

Abstract

This research explores the application of graph-based optimization techniques to enhance yield management and minimize transportation costs in industrial operations, particularly focusing on mining. By representing mining sites and processing plants as nodes and transportation routes as edges in a graph, we formulated an optimization problem aimed at maximizing yields while minimizing associated costs. Utilizing linear programming, we demonstrated significant cost savings, reducing transportation costs from 2100 units to 1700 units through optimized flow distribution. The study integrates elements of graph theory, optimization algorithms, and machine learning, providing a robust framework for efficient resource allocation and operational planning. The numerical example underscores the practical applicability of these techniques, paving the way for further research and refinement to accommodate additional constraints and dynamic changes in resource availability. This research highlights the potential of graph-based methods to achieve substantial economic and operational improvements across various industrial contexts.
Optimizing supply chain efficiency: Advanced decision support systems for enhanced performance Judijanto, Loso; Lemos, Sgarbossa Carlo; Sihotang , Jonhariono; Sihotang , Hengki Tamando
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.Vol16.2024.857.pp185-198

Abstract

This research investigates the optimization of supply chain efficiency through the application of advanced Decision Support Systems (DSS), focusing on minimizing operational costs while maintaining high service levels. The main objective is to explore how DSS, integrated with real-time data, artificial intelligence (AI), and machine learning (ML), can enhance decision-making processes across production, inventory management, and transportation. The research employs a multi-objective optimization model, developed to minimize production, inventory, transportation, and shortage costs, while dynamically adjusting decisions based on real-time demand and supply data. A numerical example is used to test the model’s effectiveness, revealing significant cost reductions in production and transportation but highlighting challenges in maintaining consistent service levels. The results indicate that DSS can substantially improve supply chain efficiency by enabling data-driven decisions in real time, though its adoption remains limited by technical and scalability challenges, particularly for small-to-medium enterprises (SMEs). This study contributes to the growing body of knowledge on supply chain optimization, offering practical insights into DSS implementation and its potential impact on operational performance. The conclusions suggest that future research should focus on developing more sophisticated DSS models capable of handling uncertainty, sustainability, and resilience, as well as enhancing scalability to make DSS more accessible to a broader range of businesses.
Advancing Decision-Making: AI-Driven Optimization Models for Complex Systems Sihotang, Hengki Tamando; Sihotang, Jonhariono; Simbolon, Agata Putri Handayani; Panjaitan, Firta Sari; Simbolon, Roma Sinta
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.v13i3.581

Abstract

Effective decision-making in complex systems requires optimization models that balance multiple competing objectives, such as cost efficiency, time constraints, and adaptability to dynamic environments. This research proposes an AI-driven optimization model utilizing the Pareto optimization algorithm to enhance decision-making accuracy and system resilience. The model was tested in a logistics scenario, demonstrating a 10% reduction in operational costs and a 36% decrease in time deviations while improving adaptability to real-time disruptions. Unlike traditional static models, the proposed framework dynamically adjusts to external factors, optimizing resource allocation and route planning in real-world conditions. The findings highlight the model’s capability to bridge the gap between theoretical AI advancements and practical applications in industries such as supply chain management, urban transportation, and disaster response logistics. While computational requirements and data availability pose challenges, future research should explore computational efficiency enhancements, broader industry applications, and sustainability integration. This study contributes to the advancement of AI-based multi-objective optimization, providing a scalable and adaptable solution for complex decision-making in dynamic environments
Analysis Of The Differences In The Use Of Zoom And Google Meet In Learning At Putra Abadi Langkat University Sihotang, Jonhariono
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalkomputer.v15i02.174

Abstract

This research aims to analyze the differences in the use of Zoom and Google Meet in learning at Putra Abadi University, Langkat. Data was collected from Putra Abadi University Langkat students during 2020-2021 with a total sample of 100 students, where 50 students represented Zoom users and 50 students represented Google Meet users. With a student population of Putra Abadi University Langkat of more than 1500 people, sampling using the Slovin formula resulted in a sample size of around 316 students. The AHP method is used to analyze student perceptions of the effectiveness and importance of using Zoom and Google Meet in distance learning. The steps of AHP involve problem identification, hierarchical decomposition, pairwise comparison, and global priority synthesis. Comparisons were made using a Likert scale to assess the level of importance between elements. The results of the questionnaire given to students of the Information Systems Program at Stabat showed that only 48 students (21.43%) were interested in studying using the Zoom platform. The main reasons why students are interested in using the platform are because the lecturers use it, the image quality is better, the sound is clearer, the background replacement is easier, and the features are easier to use. There are many drawbacks to Zoom, such as the 40-minute time limit, that make it unappealing to students. On the other hand, Zoom has advantages, such as the ability to be downloaded for free with participation of up to 100 people, and the ability to schedule Zoom Live and record or save videos. The results of this research are different from the results of previous research which concluded that there was no difference in students' interest in using the Zoom Meeting and G-Meet applications. In addition, other research states that are based on student analysis, Zoom is more preferred as a learning tool than Google Meet. However, this research supports the conclusion of previous research that there are differences in student learning outcomes using G-Meet.
Decision Support System for Determining Infrastructure Project Priorities Using AHP and TOPSIS Methods in Deli Serdang Regency Sihotang, Jonhariono; Napitupulu, Dea Okviar Egano
INFOKUM Vol. 13 No. 05 (2025): Infokum
Publisher : Sean Institute

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Abstract

Prioritizing infrastructure projects is a major challenge in regional development planning, particularly when faced with limited resources and complex assessment criteria. This study aims to design a decision support system that can assist policymakers in determining the most deserving infrastructure projects for prioritization. The method used is a combination of the Analytic Hierarchy Process (AHP) to determine criteria weights and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank alternative projects. Five main criteria are used in this study: cost, urgency, social benefits, land readiness, and conformity with the Regional Medium-Term Development Plan (RPJMD). This study uses simulation data on ten alternative infrastructure projects in Deli Serdang Regency. The AHP results show that urgency (29.9%) and conformity with the RPJMD (25.6%) are the criteria with the highest weights. The consistency value (CR = 0.0333) indicates that the criteria assessment is carried out consistently. Through TOPSIS, it was found that the Health Center B Development project had the highest preference value (0.8176), followed by Terminal I Revitalization and Connecting Bridge E. This study proves that the integration of the AHP and TOPSIS methods is able to provide a rational, transparent, and applicable decision-making framework in the context of regional infrastructure development planning.
Analysis Of Shortest Path Determination By Utilizing Breadth First Search Algorithm Sihotang, Jonhariono; Simamora, Siska
Jurnal Info Sains : Informatika dan Sains Vol. 10 No. 2 (2020): September, Informatics and Science
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (592.99 KB) | DOI: 10.54209/infosains.v10i2.30

Abstract

The rapid development of science requires the public to keep up with the development of such technology. The use of computers as one of the tools used to facilitate work and improve the efficiency and effectiveness of work is also high, this we can see from the development of such technology. Artificial Intelligence (AI) is one part of computer science that learns about how to make computers can do the job as humans do. At the beginning of its creation, the computer was only functioned as a counting tool. But along with the development of the times, the role of computers increasingly dominates the life of mankind. Computers are no longer only used as a calculation tool, more than that, computers are expected to be empowered to do everything that can be done by humans. People can be good at solving all problems in this world because people have knowledge andexperience. Knowledge is gained from learning. The more knowledge possessed by a person is certainly expected to be more able to solve problems. But the provision of knowledge alone is not enough, people are also given the sense to reason, draw conclusions based on their knowledge andexperience.
Early warning systems for financial distress: A machine learning approach to corporate risk mitigation Judijanto, Loso; Sihotang, Jonhariono; Simbolon, Agata Putri Handayani
International Journal of Basic and Applied Science Vol. 13 No. 1 (2024): June: Basic and Aplied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i1.470

Abstract

This research explores the development of an early warning system for corporate financial distress using machine learning techniques to address key challenges in corporate risk mitigation. The main objective is to enhance predictive accuracy by integrating financial and non-financial data, addressing class imbalance, and ensuring model interpretability. The research design involves the formulation of a new machine learning model, leveraging cost-sensitive learning and feature selection, and is tested with a numerical example using logistic regression. Methodologically, the study adopts a data-driven approach that incorporates diverse financial ratios, macroeconomic variables, and market sentiment indicators to predict corporate distress. The numerical results from a basic logistic regression model demonstrate poor performance, especially in handling class imbalance, revealing limitations in traditional statistical models. However, the research suggests that machine learning methods, particularly ensemble learning with cost-sensitive algorithms, offer superior predictive accuracy and practical applicability. The study concludes that integrating advanced techniques and diverse datasets leads to more reliable early warning systems, with significant implications for corporate governance and financial risk management. Future research should explore more sophisticated machine learning models and extend real-world applications across various industries and economic conditions.
Meta-Learning Algorithms for Resource-Constrained Intelligent IoT Devices Riandari, Fristi; Sihotang , Jonhariono
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 4 (2024): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid expansion of the Internet of Things (IoT) requires devices that can operate intelligently in dynamic environments despite severe hardware and energy constraints. Traditional machine learning models deployed on microcontroller-class IoT devices often struggle to adapt to new tasks, handle sensor noise, and maintain accuracy under changing environmental conditions. This research proposes a lightweight meta-learning framework specifically optimized for resource-constrained IoT platforms, combining gradient-based meta-learning techniques with model compression strategies such as quantization and pruning. The objective is to enable rapid few-shot adaptation, reduce computational overhead, and ensure robust performance in real-world IoT deployments. The study adopts a hardware-aware design approach, implementing the proposed model on ultra-low-power microcontrollers such as ARM Cortex-M series and ESP32. A two-phase training pipeline meta-training and on-device fine-tuning is used to evaluate adaptation speed, latency, memory footprint, accuracy, and energy consumption. Experimental results demonstrate that the lightweight meta-learning model adapts to new sensor-based tasks significantly faster than conventional supervised learning models while consuming substantially less energy. The model also shows improved resilience to environmental variations and sensor noise, outperforming baseline TinyML and standard meta-learning architectures under constrained conditions. Despite these promising results, the research identifies limitations related to computational cost, memory usage during adaptation, and the trade-off between model complexity and predictive accuracy. Nonetheless, the findings highlight the potential of meta-learning as a transformative approach for building intelligent, adaptive, and energy-efficient IoT systems. This study contributes to the advancement of TinyML and edge intelligence by providing a practical and scalable meta-learning solution tailored for ultra-low-power IoT devices.
A Unified Hybrid AHP, Utility, TOPSIS Decision Model for Enhancing Ranking Reliability in Complex Multi-Criteria Problems Sihotang, Jonhariono; Batubara, Juliana
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This study proposes a unified mathematical framework that integrates the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Utility Theory to enhance multi-criteria decision-making (MCDM) in complex environments. While AHP provides a structured mechanism for deriving criterion weights, TOPSIS offers an effective geometric ranking approach, and Utility Theory captures nonlinear preferences and risk attitudes. However, these methods often operate independently, resulting in inconsistent rankings and incomplete representation of decision-maker behavior. The proposed framework bridges these gaps by combining AHP-derived weights, utility-transformed criterion values, and TOPSIS proximity measures into an integrated decision function. A numerical case study illustrates the full application of the model, including weight calculation, utility transformation, ideal-solution analysis, and composite scoring. Results show that the unified model produces more stable and discriminative rankings than pure AHP, pure TOPSIS, or pure Utility Theory. Sensitivity and robustness analyses further demonstrate that the integrated approach maintains ranking consistency under variations in weights, normalization methods, and utility parameters. Comparative validation using Spearman correlation confirms strong agreement with established methods while improving resilience to uncertainty. Overall, this research contributes a comprehensive and theoretically grounded MCDM framework that better reflects human judgment, strengthens ranking reliability, and is adaptable to diverse decision contexts. The unified model offers a powerful tool for practitioners and researchers seeking more accurate and robust decision support in multi-criteria environments.
A Dynamic Decision-Making Model for Regional Governance Based on Adaptive Preference Learning Sihotang, Jonhariono; Batubara, Juliana
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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

Abstract

This research develops a dynamic decision-making model for regional governance based on adaptive preference learning to address the limitations of traditional static policy frameworks. The study integrates decision theory, reinforcement learning, Bayesian preference modeling, and multi-criteria decision-making (MCDM) into a unified system capable of capturing evolving stakeholder preferences and responding to rapidly changing socio-economic conditions. The model consists of four core components data input layer, preference learning engine, policy decision module, and real-time feedback system which collectively enable continuous updating of decision parameters and ongoing evaluation of policy outcomes. Using a mixed-method approach that combines stakeholder surveys, historical governance data, performance indicators, and computational simulations, the study demonstrates that the adaptive model significantly improves decision accuracy, responsiveness, and alignment with citizen needs. The system’s dynamic feedback loops allow policies to be refined in real time, enhancing predictive capability and reducing the risks associated with rigid or outdated policy assumptions. Results show that the model outperforms traditional governance approaches in terms of decision efficiency, data-driven fairness, and the ability to anticipate emerging issues. Although challenges remain such as data sparsity, computational complexity, infrastructure limitations, and potential resistance from policymakers the findings highlight the model’s practical value for modern regional governance. The research contributes theoretically by advancing the application of adaptive learning in public policy decision-making and practically by offering a framework that supports faster, smarter, and more citizen-centric governance. Overall, the study underscores the potential of adaptive preference learning to transform regional decision-making in increasingly complex and uncertain environments.
Co-Authors A, Galih Prakoso Rizky Aisyah Alesha Amran Manalu Batubara, J Blackhurst, Alcívar Colicchia, Cagno Stone Dea Okviar Egano Napitupulu Desi Vinsensia Erwin Setiawan Panjaitan Firta Sari Panjaitan Fristi Riandari Hengki Tamando Sihotang Indradewa, Rhian Judijanto, Loso Juliana Batubara Juliana Batubara Lemos, Sgarbossa Carlo Manalu, Amran Panjaitan, Firta Sari Patrisius Michaud Felix Marsoit Roma Sinta Simbolon Roni Yunis Simamora, Siska Simbolon, Agata Putri Handayani Simbolon, Roma Sinta Simbolon, Romasinta Siskawati Amri Sonya Enjelina Gorat Vinsensia, Desi