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All Journal Jurnal Teknik Informatika C.I.T. Medicom International Journal of Basic and Applied Science Jurnal Ilmu Pendidikan dan Humaniora Law and Economics
Simbolon, Roma Sinta
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Physics Social Sciences

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Enhancing Maternal and Infant Health: Improving Healthcare Access through Cultural Sensitivity and Community Engagement in Tigalingga, Dairi Regency Amri, Siskawati; Simbolon, Roma Sinta
Law and Economics Vol. 17 No. 1 (2023): February: Law and Economics
Publisher : Institute for Law and Economics Studies

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/laweco.v17i1.42

Abstract

This research investigates the state of maternal and infant health in Tigalingga, Dairi Regency, emphasizing the significance of reducing mortality rates among mothers and infants in this underserved community. Employing a mixed-method approach, the study combines quantitative analysis and qualitative insights to understand the multifaceted landscape of healthcare challenges. Utilizing regional health records, interviews, and community engagement, the study unveils critical disparities, cultural influences, and barriers to healthcare access. The findings showcase higher-than-desired maternal and infant mortality rates, underscoring the urgency for targeted interventions. The Health Assistance Program, a focal point of the research, demonstrates promising outcomes in reducing mortality rates. The program's success is attributed to improved healthcare access, culturally sensitive interventions, and community engagement. Recommendations derived from the study advocate for sustained efforts in healthcare infrastructure development, enhanced community involvement, and cultural adaptation in healthcare initiatives. The implications of the research extend to public health policy, emphasizing the importance of equitable healthcare access, culturally competent interventions, and community-driven strategies. The research underscores the critical need for tailored healthcare interventions, respecting cultural practices and addressing healthcare disparities, to improve the health outcomes of mothers and infants in Tigalingga. The findings serve as a foundation for informed decision-making and the development of targeted interventions not only within the region but also for similar vulnerable communities globally
Enhancing Food Security and Alleviating Poverty: Community Responses to the Poor Rice Program in Jaranguda Village, Indonesia Simbolon, Roma Sinta; Shylendra, Finan
Law and Economics Vol. 16 No. 1 (2022): February: Law and Economics
Publisher : Institute for Law and Economics Studies

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/laweco.v16i1.48

Abstract

This study evaluates the impact of the Poor Rice Program in Jaranguda Village, located in the Merdeka District of the Karo Regency, Indonesia. The research employs a mixed-method approach, combining quantitative and qualitative analyses to comprehensively assess the program's effectiveness in addressing food insecurity, reducing household expenses, and engaging the community. Quantitative analysis of household surveys revealed a substantial reduction, averaging 30%, in monthly food expenses among program beneficiaries. Moreover, the provision of subsidized or free rice significantly decreased reported food insecurity incidents by 40%, ensuring a more stable and consistent food supply. Qualitative data from community feedback highlighted a strong sense of appreciation for the program's assistance, along with suggestions for improved distribution mechanisms and increased community engagement. These insights emphasize the need for ongoing dialogue and program adaptability to better cater to the evolving needs of the community. The findings underscore the significant impact of the Poor Rice Program in alleviating financial burdens and improving food security within Jaranguda Village. The reduction in household expenses and enhanced food security among beneficiaries signify the tangible outcomes of targeted interventions, emphasizing the importance of responsive, community-driven programs in addressing poverty-related challenges. This research contributes empirical evidence supporting the effectiveness of poverty-alleviation initiatives, providing valuable insights for policy development and future interventions in similar contexts. The findings offer a foundation for guiding community-driven programs aimed at improving the well-being of vulnerable populations.
Analyzing the Impact of Social Assistance Programs on Poverty Alleviation in Karo Regency Simbolon, Roma Sinta
Law and Economics Vol. 16 No. 3 (2022): Oktober: Law and Economics
Publisher : Institute for Law and Economics Studies

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/laweco.v16i3.58

Abstract

This research delves into an in-depth analysis of the impact of social assistance programs on poverty alleviation within Karo Regency. Employing a mixed-methods approach encompassing qualitative and quantitative methodologies, the study investigates the effectiveness and nuances of these initiatives in addressing multifaceted dimensions of poverty. The research reveals multifaceted insights, showcasing both successes and limitations of social assistance programs. While the initiatives demonstrated commendable improvements in household incomes, access to essential services, and socio-economic indicators, nuanced outcomes emerged, revealing disparities among demographic groups, unintended consequences like dependency on aid, and the interplay of cultural context in program effectiveness. Drawing implications from the findings, the research suggests targeted policy modifications, emphasizing inclusivity, sustainability, empowerment, and community engagement within social assistance programs. The study contributes valuable insights to the discourse on poverty alleviation, highlighting the need for a more nuanced and participatory approach to enhance the impact of social assistance initiatives in Karo Regency.
Unveiling the Past: LiDAR Technology's Role in Discovering Hidden Ar-chaeological Sites Simbolon, Roma Sinta; Comer, Alday
Jurnal Ilmu Pendidikan dan Humaniora Vol. 12 No. 1 (2023): Jan: Education and Humanities
Publisher : Insan Akademika Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jiph.v12i1.28

Abstract

This research delves into the transformative role of LiDAR (Light Detection and Ranging) technology in archaeological exploration, with a primary focus on the discovery of hidden archaeological sites. LiDAR has emerged as a game-changing tool, offering unprecedented advantages in the field, including rapid data collection, the penetration of coverings and vegetation, and a non-invasive approach to archaeological research. Through this technology, a multitude of hidden archaeological features, such as lost cities, intricate urban planning, extensive road networks, agricultural practices, and defensive structures, have been unveiled. These discoveries have rewritten historical narratives, reshaped our understanding of the past, and underscored the significance of cultural heritage preservation and sustainable land use practices. LiDAR's efficiency and accuracy have enhanced the speed and precision of data collection, making it an essential tool for future archaeological studies. Its non-invasive nature respects the integrity of archaeological sites, and its multidisciplinary collaborations expand the horizon of research. The recognition of the cultural and spiritual significance of hidden archaeological sites, particularly in indigenous regions, has influenced future research approaches. Insights into ancient agricultural practices and sustainable land use have the potential to guide contemporary practices and environmental conservation. LiDAR technology continues to evolve, promising even more efficient and accurate data acquisition, thereby further deepening our understanding of the past and enhancing the future of archaeological research.
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
A Probabilistic Decision Model for AI-Driven Optimization in Highly Complex Stochastic Mixed-Integer Nonlinear Programming (MINLP) Systems Sihotang, Hengki Tamando; Simbolon, Roma Sinta
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 present significant challenges for optimization, particularly when operating under uncertainty, high dimensionality, and dynamic environmental conditions. This study proposes a probabilistic decision model designed to enhance AI-driven optimization by integrating uncertainty quantification, adaptive decision mechanisms, and robust probabilistic reasoning. The methodology combines probabilistic modeling with machine learning techniques and is evaluated through a series of controlled experimental scenarios that simulate real-world complexity and noise. The results indicate substantial improvements in decision accuracy, solution stability, and robustness compared to traditional deterministic and heuristic-based optimization methods. The model consistently maintains high performance despite uncertain inputs and fluctuating system parameters, demonstrating its reliability in environments where conventional approaches tend to degrade. Theoretical analysis further validates the model’s feasibility and guarantees performance consistency under uncertainty. Overall, this research contributes a scalable and resilient decision-making framework capable of addressing the limitations of existing optimization models, offering significant potential for broad application in AI-driven complex systems.
Development of a Robust–Stochastic Optimization Framework for Enhancing Stability and Efficiency in Transportation Models Sihotang, Hengki Tamando; Simbolon, Roma Sinta
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 study develops a unified robust stochastic optimization framework designed to enhance the stability, efficiency, and reliability of transportation models operating under significant uncertainty. Traditional deterministic, robust-only, and stochastic-only approaches each face limitations deterministic models fail under variability, robust models tend to be overly conservative, and stochastic models struggle under extreme disruptions. To address these gaps, the proposed framework integrates worst-case uncertainty sets with probabilistic scenario modeling, enabling decisions that remain feasible under extreme conditions while maintaining optimal performance during typical operations. The methodology includes comprehensive uncertainty modeling of travel time fluctuations, demand variability, cost changes, and network disruptions; a hybrid mathematical formulation combining robust constraints with stochastic scenarios; and an efficient algorithmic structure employing enhanced decomposition techniques and scenario filtering to reduce computational complexity. Experimental results using benchmark and real-world transportation datasets show significant improvements in solution stability, travel time reliability, cost efficiency, and network resilience compared with conventional models. The hybrid framework reduces over-conservatism, lowers operational cost by up to 25%, and increases robustness under high-variability conditions, demonstrating superior performance in both normal and disrupted environments. The study advances optimization theory by offering a scalable and computationally tractable integration of two major uncertainty-handling paradigms, while contributing to transportation modeling through a practical tool capable of supporting reliable routing, scheduling, and logistics planning. Overall, this research provides a robust and adaptive optimization strategy that strengthens decision-making under uncertainty and improves the resilience of modern transportation systems.
A Fundamental Multilevel Optimization Decision Model for Complex Systems Based on an AI-Optimization Fusion Framework Sihotang, Hengki Tamando; Simbolon, Roma Sinta
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

Complex systems in modern domains such as transportation, energy, supply chains, and autonomous multi-agent networks require decision-making frameworks capable of handling hierarchical structures, dynamic environments, and high levels of uncertainty. Traditional multilevel optimization models offer a structured approach but often struggle with computational complexity, nonlinear interactions, and incomplete information. This research proposes a fundamental multilevel optimization decision model based on an AI-Optimization Fusion Framework designed to overcome these limitations. The model integrates bilevel and trilevel hierarchical structures with artificial intelligence learning paradigms, including supervised learning, deep learning, and reinforcement learning, to form a unified architecture that adapts to evolving system behaviors. A hybrid algorithmic formulation is developed to merge optimization procedures with learning-based approximations, enabling faster convergence, improved robustness, and enhanced decision quality. The experimental and simulation results demonstrate that the proposed framework outperforms traditional optimization approaches in accuracy, computational efficiency, scalability, and resilience under uncertainty. The model’s hierarchical decision mechanisms allow for dynamic coordination across decision levels, while AI-driven components provide predictive and adaptive capabilities that mitigate complexity in high-dimensional environments. The research contributes a novel integrated architecture, theoretical enhancements in multilevel decision modeling, and algorithmic innovations for hybrid AI–optimization systems. Limitations related to data availability, computational resources, and structural assumptions are acknowledged, offering directions for future exploration. Overall, this study establishes a new foundation for intelligent, scalable, and robust decision-making in complex systems, positioning AI–optimization integration as a key enabler for next-generation autonomous and adaptive decision frameworks.
A Mathematical Framework for Integrating Neural Networks into Stochastic DEA Models to Reduce Variance and Improve Prediction Stability Sihotang, Hengki Tamando; Simbolon, Roma Sinta
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 study proposes a novel mathematical framework that integrates neural networks into Stochastic Data Envelopment Analysis (SDEA) to reduce variance and enhance the stability of efficiency prediction under uncertainty. Traditional DEA models rely on linear or piecewise-linear frontiers and are highly sensitive to noise, resulting in unstable efficiency scores and unreliable rankings. The proposed hybrid framework addresses these limitations by combining stochastic frontier modeling, noise-distribution assumptions, and neural network function approximation to construct a smooth, flexible, and noise-resilient efficiency frontier. Neural components capture nonlinear relationships among inputs and outputs, while regularization and bootstrapping techniques stabilize estimation and mitigate variance inflation. Empirical experiments demonstrate that the integrated model outperforms classical DEA, stochastic DEA, and bootstrap-corrected DEA in terms of variance reduction, robustness to noise, and stability across repeated sampling. Efficiency scores exhibit narrower confidence intervals, more consistent DMU rankings, and improved frontier curvature representation. Sensitivity analyses further show that the model remains robust under different noise structures and hyperparameter settings. The findings highlight the potential of combining machine learning with stochastic optimization to advance the methodological foundation of DEA. By enhancing frontier flexibility and reducing noise-induced bias, the proposed framework provides a more reliable tool for efficiency evaluation in complex and uncertain production environments. Future work should focus on enhancing interpretability, reducing computational cost, and relaxing distributional assumptions to further extend the applicability of this hybrid approach.
Co-Authors Comer, Alday Hengki Tamando Sihotang Panjaitan, Firta Sari Shylendra, Finan Sihotang, Hengki Tamando Sihotang, Jonhariono Simbolon, Agata Putri Handayani Siskawati Amri