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All Journal Jurnal Teknik Informatika C.I.T. Medicom International Journal of Basic and Applied Science Inspirasi & Strategi (INSPIRAT) : Jurnal Kebijakan Publik & Bisnis Journal of Computer Science and Research International Journal of Enterprise Modelling Law and Economics
Panjaitan, Firta Sari
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Transportation

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Stochastic modeling and performance analysis of multi-altitude LEO satellite networks using cox point processes Panjaitan, Firta Sari
International Journal of Enterprise Modelling Vol. 17 No. 1 (2023): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (448.732 KB) | DOI: 10.35335/emod.v17i1.72

Abstract

The research focuses on the stochastic modeling and performance analysis of multi-altitude Low Earth Orbit (LEO) satellite networks using Cox point processes. LEO satellite networks have emerged as a promising solution for global connectivity, offering high data rates and low latency. To optimize their performance and resource allocation, accurate modeling and analysis techniques are crucial. This research employs Cox point processes to model the spatial distribution and behavior of satellites at different altitudes within the network. The intensity functions capture the expected number of satellites per unit area at each altitude. Realizations of the Cox point process are generated using Monte Carlo simulations, enabling performance analysis in terms of network connectivity, coverage probability, signal quality, and interference levels. The results provide insights into network behavior and inform network design decisions, including the optimal number of satellites, their altitudes, and their spatial distribution. The research contributes to the advancement of multi-altitude LEO satellite networks, enabling efficient global connectivity and addressing communication needs in various industries and applications
Analyzing Police Communication Strategies for Preventing Teenage Drug Abuse in Simpang Kanan Subdistrict, Rokan Hilir Regency Jollyta, Jollyta; Denny, Denny; Simbolon, Romasinta; Panjaitan, Firta Sari
Law and Economics Vol. 16 No. 2 (2022): June: Law and Economics
Publisher : Institute for Law and Economics Studies

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/laweco.v16i2.56

Abstract

This research delves into the efficacy of police communication strategies in combating drug abuse among teenagers in Simpang Kanan Subdistrict, situated within the Rokan Hilir Regency of Indonesia. Utilizing a mixed-methods approach, the study navigates the intricate interplay of cultural dynamics, community engagement, and the effectiveness of varied communication channels employed by law enforcement agencies. Quantitative analysis unveils statistical trends in drug abuse prevalence among teenagers, shedding light on the impact of specific communication strategies. Concurrently, qualitative exploration delves into perceptions, experiences, and stakeholder feedback, offering depth to the understanding of the multifaceted issue. Findings underscore the strengths of community engagement initiatives and diverse communication channels, showcasing heightened awareness and positive behavioral shifts among teenagers. Yet, limitations such as data constraints, sampling intricacies, and resource limitations temper the comprehensiveness of the analysis. The implications drawn from this research advocate for tailored, culturally sensitive approaches, community-driven interventions, and continuous evaluation mechanisms in future policy changes. These insights transcend regional boundaries, offering a framework adaptable to analogous contexts worldwide. This research serves as a beacon, illuminating pathways toward combating teenage drug abuse.
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
Study on the Implementation of E-Government at the Village Level and Its Impact on Public Services Panjaitan, Firta Sari; Batubara, Juliana; Sinta, Roma
Inspirasi & Strategi (INSPIRAT): Jurnal Kebijakan Publik & Bisnis Vol. 16 No. 1 (2025): July: Kebijakan Publik & Bisnis
Publisher : IHSA Institute

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Abstract

This study investigates the implementation of e-government at the village level and its impact on public service delivery. With the increasing integration of digital technologies in governance, understanding the effectiveness of e-government initiatives is crucial for enhancing citizen engagement and improving service efficiency. The research employs a mixed-methods approach, combining quantitative surveys and qualitative interviews to assess user satisfaction, service accessibility, and transparency in local governance. The findings reveal that e-government significantly enhances service efficiency, with reduced waiting times and streamlined processes, leading to increased user satisfaction. However, challenges such as the digital divide, data privacy concerns, and varying levels of digital literacy among residents present obstacles to full engagement. The study emphasizes the need for targeted investments in digital infrastructure and literacy programs to ensure equitable access to e-government services. By addressing these challenges, policymakers and local governments can harness the full potential of e-government to foster more inclusive and transparent public service delivery, ultimately strengthening community trust and engagement. This research contributes to the growing body of literature on e-government, providing insights into the dynamics of digital governance in rural contexts and highlighting best practices for effective implementation.
Enhancing Electoral Decision-Making: A Social Learning Network Election Decision Support System Utilizing AHP and PROMETHEE Methods Alesha, Aisyah; Simbolon , Romasinta; Batubara, Juliana; Panjaitan, Firta Sari
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 1 (2024): Jan: CNN and Artificial
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.v2i1.36

Abstract

This In today's digital age, the intersection of technology, democracy, and citizen participation has become increasingly prominent. This research explores the development and application of a Social Learning Network Election Decision Support System (SLNEDSS) using Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) methods to enhance electoral decision-making processes. By leveraging social learning networks as platforms for information dissemination and deliberative discourse, SLNEDSS empowers citizens to make informed choices that reflect their values, aspirations, and preferences. The integration of AHP and PROMETHEE methods within SLNEDSS provides users with structured frameworks for evaluating electoral alternatives, synthesizing stakeholder preferences, and facilitating transparent and systematic decision-making processes. Through empirical studies, the effectiveness of SLNEDSS in enhancing the quality and inclusivity of electoral outcomes is demonstrated, highlighting its transformative potential in shaping the future of democratic governance. The research also identifies challenges and limitations associated with SLNEDSS, such as algorithmic biases and user adoption, and suggests directions for future research to address these shortcomings. Ultimately, this research contributes to advancing the frontiers of knowledge and innovation in the field of electoral decision support systems, paving the way for a more informed, inclusive, and responsive democracy in the digital age.
Dynamic Latent State Modeling for Predicting Public Behavior in Digital Ecosystems Panjaitan, Firta Sari; Batubara, Juliana
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

This study proposes a Dynamic Latent State Modeling (DLSM) framework to predict public behavior within rapidly evolving digital ecosystems. As online interactions grow increasingly complex shaped by algorithmic exposure, platform norms, and sociopolitical events traditional static models fail to capture the fluidity and nonlinearity of user behavior. Using a combination of Hidden Markov Models, state-space modeling, and probabilistic clustering, this research identifies latent behavioral states underlying observable digital activities such as posting frequency, sentiment shifts, network engagement, and information consumption patterns. Results reveal four major latent states Passive Observation, Selective Engagement, Active Participation, and Reactive Mobilization each corresponding to meaningful psychological and social modes of online behavior. Transition matrices demonstrate that users shift states in response to contextual triggers including emotional content exposure, social reinforcement, platform incentives, and external offline events. The DLSM framework outperforms baseline machine learning classifiers by capturing temporal dependencies and hidden motivational structures influencing online actions. The study offers important implications for digital governance, policy design, crisis communication, marketing strategy, and misinformation management, particularly in anticipating rapid escalations in public sentiment or mobilization. However, limitations include potential dataset biases, constraints on generalizability across platforms, and challenges in detecting synthetic or automated behavior (bots) embedded within user streams. Overall, the research contributes a robust, interpretable, and dynamic approach to understanding and predicting public behavior in complex digital environments.
A Probabilistic Decision Model for AI-Driven Optimization in Highly Complex Systems Riandari, Fristi; Panjaitan, Firta Sari
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

Highly complex systems such as smart grids, autonomous transportation networks, and large-scale supply chains present significant challenges for optimization due to high dimensionality, nonlinear interactions, and pervasive uncertainty. Traditional deterministic models often fail under dynamic conditions, while many AI-based approaches lack robustness and stability when confronted with noisy or incomplete data. Addressing these issues, this study proposes a probabilistic decision model designed to enhance AI-driven optimization in uncertain and rapidly changing environments. The model integrates probabilistic graphical structures, Bayesian inference, and AI-based optimization techniques to quantify uncertainty and support adaptive decision-making. Experimental evaluations were conducted using a combination of synthetic datasets, simulation environments, and benchmark scenarios representative of real-world complex systems. Results show that the proposed model achieves significantly higher decision accuracy, improved stability under noisy conditions, and more efficient performance in high-dimensional settings compared with classical optimization, reinforcement learning, and standard probabilistic approaches. The model consistently reduces uncertainty and delivers robust, reliable solutions across a wide range of test conditions.The study presents a scalable, interpretable, and highly effective framework for uncertainty-aware optimization. Its strong performance and generalizability highlight its potential for deployment in critical real-world applications where reliability, safety, and adaptability are essential.
A Probabilistic Decision Model for AI-Driven Optimization in Highly Complex Systems Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 2 (2025): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This research proposes a novel Probabilistic Decision Model (PDM) designed to address the challenges of optimization in highly complex systems characterized by high-dimensional states, nonlinear interactions, and deep uncertainty. Traditional deterministic, heuristic, and deep learning-based methods often fail to provide reliable decisions under such conditions due to their limited scalability, lack of uncertainty quantification, or inability to guarantee constraint satisfaction. The proposed model integrates probabilistic constraints, expectation-based objective functions, and adaptive AI-driven scenario generation to deliver a robust and flexible optimization framework. A rigorous mathematical formulation is presented, including probability space definitions, risk measures, and feasible neighborhood rules. Validation through numerical simulations demonstrates that the model maintains high feasibility, reduces worst-case risks, and remains stable even under extreme uncertainty. Case studies in smart grid optimization, logistics routing, and manufacturing scheduling further highlight significant performance improvements over classical stochastic optimization, MDP/POMDP models, and deep reinforcement learning without probabilistic modeling. The results confirm the model’s strong scalability, enhanced uncertainty modeling, and practical relevance for real-world industrial environments. This research contributes a hybrid probabilistic-AI framework that advances the reliability, resilience, and intelligence of decision-making in modern complex systems, while opening pathways for future exploration in multi-agent coordination, automated parameter tuning, and real-time adaptive optimization.
Theoretical Advances in Hungarian Maximization Models for Multi-Site Human Resource Allocation Riandari, Fristi; Panjaitan, Firta Sari
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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Abstract

This study presents a theoretical and methodological advancement of the Hungarian maximization model for optimizing multi-site human resource allocation. Traditional Hungarian algorithms focus on single-site, cost-minimization assignments, limiting their applicability in modern workforce environments characterized by distributed operations and diverse employee attributes. To address these gaps, the study reformulates the classical objective function into a maximization framework and incorporates multi-site constraints, multi-criteria employee attributes, and workload balancing requirements. The enhanced model is evaluated through mathematical analysis and simulation-based case studies to assess its performance relative to baseline assignment and heuristic optimization methods. The results demonstrate that the proposed model achieves higher organizational productivity, reduces operational costs, improves staff distribution equity, and significantly accelerates computation time compared with existing approaches. Moreover, the model ensures more consistent alignment between employee capabilities and site-level demands, offering a more robust foundation for strategic workforce deployment. Comparisons with previous studies show that this research provides the first Hungarian-based maximization framework specifically tailored for multi-site HR allocation, overcoming key limitations related to scalability, fairness, and optimality. Overall, this study contributes a rigorous theoretical extension of the Hungarian method and offers practical implications for workforce scheduling, supply-chain staffing, healthcare deployment, and emergency response operations. The findings underscore the potential of deterministic optimization models to support intelligent and equitable human resource decision-making in increasingly complex organizational settings.
A Unified Mathematical Framework for NWC, MODI, and Stepping Stone as Foundational Models in Optimal Transport Theory Riandari, Fristi; Panjaitan, Firta Sari
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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Abstract

This research introduces a unified mathematical framework connecting three classical transportation problem methods Northwest Corner Rule (NWC), Modified Distribution Method (MODI), and the Stepping Stone Method to the modern theory of Optimal Transport (OT). Despite their long-standing use in operations research, these classical algorithms have traditionally been treated as heuristic procedures without a formal theoretical link to the rigorous Monge Kantorovich formulation. This study demonstrates that each method corresponds directly to fundamental geometric and dual structures of the transportation polytope: NWC generates an initial extreme-point solution, MODI computes dual potentials analogous to Kantorovich potentials, and Stepping Stone identifies improvement cycles consistent with movements along polytope edges. Using formal definitions, algebraic mappings, and geometric interpretation, the research establishes a coherent connection between classical OR algorithms and OT duality theory. The results show that these methods are not isolated heuristics, but structured approximations of optimal transport processes. The unified framework improves theoretical understanding, simplifies instructional explanations, and offers methodological insights that may support future algorithmic enhancements. Limitations include scalability challenges and reduced applicability to complex continuous OT settings. Overall, this research contributes a foundational unification that bridges classical transportation algorithms with contemporary optimal transport theory, advancing both theoretical rigor and practical comprehension.
Co-Authors Alesha, Aisyah Denny Denny Hengki Tamando Sihotang Jollyta, Jollyta Juliana Batubara Riandari, Fristi Sihotang, Jonhariono Simbolon , Romasinta Simbolon, Agata Putri Handayani Simbolon, Roma Sinta Simbolon, Romasinta Sinta, Roma