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Optimizing Data-Based Decision Making: Development And Implementation Of Decision Support System In Langkat Regency Government Sihotang, Jonhariono; Paska Marto Hasugian
INFOKUM Vol. 12 No. 04 (2024): Engineering, Computer and Communication, November 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v12i04.2807

Effective decision-making in local government is essential to improve the quality of public services and policies. The Langkat Regency Government faces challenges in managing data efficiently due to the limited integrated information system. This study aims to develop and implement a Decision Support System (DSS) to support data-based decision-making in local government. Using a mixed-method approach, data were collected through observation, interviews, questionnaires, and document analysis from related agencies. The developed DSS prototype integrates cloud computing and artificial intelligence to accelerate data analysis and generate policy recommendations. The results showed that DSS increased data processing time efficiency and decision accuracy by 40%. However, challenges such as resistance to change and limited infrastructure are still obstacles. Therefore, a strategy to increase human resource capacity and digitalization policies is needed to ensure the sustainability of DSS.

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

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

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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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

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.

New Method for Identification and Response to Infectious Disease Patterns Based on Comprehensive Health Service Data Vinsensia, Desi; Amri, Siskawati; Sihotang, Jonhariono; Sihotang, Hengki Tamando
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.4000

Infectious diseases continue to pose a major threat to global public health and require early detection and effective response strategies. Despite advances in information technology and data analysis, the full potential of health data in identifying disease patterns and trends remains underutilised. This study aims to propose a comprehensive new mathematical model (new method) that utilises health data to identify infectious disease patterns and trends by exploring the potential of data-driven care approaches in addressing public health challenges associated with infectious diseases. The research methods used are exploratory data collection and analytical model development. The research results obtained mathematical models and algorithms that consider data of period, time, patterns, and trends of dangerous diseases, statistical analysis, and recommendations. Data visualisation and in-depth analysis were conducted in the research to improve the ability to respond to infectious disease threats and provide better decision-making solutions in improving outbreak response, as well as improving preparedness in addressing public health challenges. This research contributes to health practitioners and decision-makers.

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

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.

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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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)

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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.

A Foundational Model for Data-Driven Decision Systems Using Probabilistic Preference Structures Sihotang, Jonhariono; Batubara, J
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 4 (2025): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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This research proposes a foundational model for data-driven decision systems based on probabilistic preference structures, addressing the limitations of traditional deterministic and utility-based approaches. The model integrates probability theory, Bayesian inference, and decision theory to represent preferences as flexible probability distributions capable of capturing uncertainty, partial orderings, and multi-attribute trade-offs. A set of novel algorithms is introduced for learning and estimating latent probabilistic preferences from noisy, incomplete, and heterogeneous data sources. These learned preference structures are embedded within an optimization framework that combines Bayesian updating with Markov decision processes, enabling the system to generate optimal decisions under uncertainty. Experimental evaluations conducted across synthetic and real-world datasets demonstrate significant improvements in accuracy, robustness, stability, and decision quality compared to existing preference modeling methods. The unified framework also enhances explainability by quantifying uncertainty and providing interpretable probabilistic outputs. The research makes theoretical contributions by establishing a mathematical ontology for probabilistic preferences, methodological contributions through the development of scalable inference and decision algorithms, and practical contributions by enabling reliable decision-making in environments characterized by inconsistent or probabilistic data. Overall, the results validate the proposed framework as a comprehensive and flexible foundation for next-generation intelligent decision systems, offering improved adaptability, reliability, and transparency in complex real-world applications.

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