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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

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Developing an Enhanced Algorithms to Solve Mixed Integer Non-Linear Programming Problems Based on a Feasible Neighborhood Search Strategy Wahyudi, Mochamad; Firmansyah, Firmansyah; Sihotang, Hengki Tamando; Pujiastuti, Lise; Mawengkang, Herman
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 2 (2023): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i2.3706

Abstract

Engineering optimization problems often involve nonlinear objective functions, which can capture complex relationships and dependencies between variables. This study focuses on a unique nonlinear mathematics programming problem characterized by a subset of variables that can only take discrete values and are linearly separable from the continuous variables. The combination of integer variables and non-linearities makes this problem much more complex than traditional nonlinear programming problems with only continuous variables. Furthermore, the presence of integer variables can result in a combinatorial explosion of potential solutions, significantly enlarging the search space and making it challenging to explore effectively. This issue becomes especially challenging for larger problems, leading to long computation times or even infeasibility. To address these challenges, we propose a method that employs the "active constraint" approach in conjunction with the release of nonbasic variables from their boundaries. This technique compels suitable non-integer fundamental variables to migrate to their neighboring integer positions. Additionally, we have researched selection criteria for choosing a nonbasic variable to use in the integerizing technique. Through implementation and testing on various problems, these techniques have proven to be successful.
A Unified Theoretical-Practical Framework for Explainable Machine Learning in Critical Public Sector Applications Sihotang, Hengki Tamando; Simbolon, Romasinta
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 adoption of machine learning (ML) in the public sector has increased the need for transparent, accountable, and trustworthy algorithmic decision-making, particularly in high-stakes domains such as social welfare, healthcare, security, and public administration. However, existing approaches to explainable machine learning (XML) remain fragmented, focusing primarily on technical explanation techniques without integrating the institutional, ethical, and user-centered requirements of government environments. This research aims to develop a unified theoretical practical framework that operationalizes explainability across the entire ML lifecycle for critical public-sector applications. This study adopts a qualitative, multi-stage research design that combines theoretical synthesis, framework construction, and empirical validation through expert assessment and case-based evaluation.The results demonstrate that explainability is a multidimensional construct that extends beyond algorithmic transparency to include contextual risk assessment, adaptive explanation delivery, and governance mechanisms such as auditability, human oversight, and documentation standards. The proposed framework integrates four interconnected layers context analysis, model design and transparency, explanation delivery, and oversight and governance providing a structured pathway for implementing explainable ML systems that meet public-sector standards of fairness, legitimacy, and accountability. Expert feedback and case evaluations confirm that the framework enhances interpretability, reduces misinterpretation risks, and supports more informed decision-making among stakeholders. This research contributes to the advancement of responsible AI in government by offering a comprehensive model that bridges technical methods with policy and practice, paving the way for more transparent and trustworthy ML adoption in public-sector services.
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol17.2025.1388.pp136-147

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): 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.Vol17.2025.1394.pp196-206

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.
Developing the Adaptive Digital IT Governance Framework for Next-Generation IT Governance Bambang Saras Yulistiawan; Rifka Widyastuti; RR Octanty Mulianingtyas; Galih Prakoso Rizky A; Hengki Tamando Sihotang
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5628

Abstract

The increasing complexity of digital transformation requires an adaptive, measurable, and contextaware IT governance model. However, existing frameworks such as COBIT, ITIL, TOGAF, and ISO/IEC 38500 tend to be partial and prescriptive, failing to address strategic, operational, and innovative needs holistically. This study proposes the Adaptive Digital IT Governance Framework, anovel governance model synthesized from eleven leading IT frameworks and structured into three integrated domains: Govern, Manage, and Adapt. Employing a Design Science Research methodology, the model was developed through a systematic framework analysis, conceptual domain formulation, iterative implementation mapping, and the design of a maturity assessment instrument. The results demonstrate that the Adaptive Digital IT Governance Framework offers a modular, scalable, and value-driven governance solution suited for diverse organizational contexts. Theoretical contributions include extending the IT governance paradigm by integrating strategic alignment, agile governance, and digital sustainability. Practically, the framework provides actionable guidance for designing, assessing, and enhancing digital governance systems across sectors. Unlike previous cross-framework synthesis efforts, the Adaptive Digital IT Governance Framework explicitly introduces the Adapt domain, operationalizing governance agility, innovation capability, and sustainability measurement. This makes the Adaptive Digital IT Governance Framework the first modular, maturity-oriented framework that simultaneously integrates strategy, operations, and adaptability, positioning it as a next-generationmodel to support organizational resilience and sustainable digital transformation.
Design and Testing of an Energy-Saving Ultrasonic Rat Repeller Prototype for Open Agricultural Environments Sihotang, Hengki Tamando; Simbolon, Roma Sinta
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 3 (2023): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Rat infestations are a major threat to agricultural productivity in open-field environments, causing significant crop damage and economic losses. Conventional control methods, such as chemical poisons and mechanical traps, are often labor-intensive, environmentally harmful, and pose risks to non-target species. This research focuses on the design, development, and testing of an energy-saving ultrasonic rat repeller prototype tailored for open agricultural fields, aiming to provide an environmentally friendly and practical pest control solution. The prototype integrates a microcontroller-based control system, ultrasonic transducer, and energy-efficient power management, including low-power modes and intermittent frequency emission to reduce energy consumption while maintaining repellent effectiveness. Laboratory testing verified frequency accuracy, operational stability, and power usage, while field testing assessed rat activity reduction, crop damage mitigation, and device endurance under varying environmental conditions. Results indicate that the prototype effectively deters rats within its coverage area, reduces crop damage, and consumes significantly less energy compared to conventional continuous-emission devices. The study demonstrates the feasibility of energy-efficient ultrasonic technology for sustainable pest management and provides a foundation for future enhancements, such as solar-powered operation, IoT-based monitoring, and multi-pest control integration.
Effectiveness of Ultrasonic Frequencies on the Behavior and Migration Patterns of Rice Field Rats (Rattus argentiventer) Sihotang, Hengki Tamando; Sihotang, Jonhariono; Simbolon, Romasinta
International Journal of Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v16i3.163

Abstract

Rat infestation by Rattus argentiventer remains a serious problem in irrigated rice fields, causing significant yield losses and threatening sustainable rice production. Conventional control methods rely heavily on chemical rodenticides, which pose environmental risks and show declining long-term effectiveness. Ultrasonic deterrent technology has been proposed as an alternative; however, its effectiveness in open-field agricultural environments remains inconsistent and poorly understood. This study aims to analyze the behavioral and migration responses of rice field rats to different ultrasonic frequency ranges to clarify the mechanisms underlying ultrasonic deterrence. A field-based experimental design was applied using paired treatment and control plots, with ultrasonic frequencies ranging from 20 to 40 kHz. Rat activity and movement were monitored through camera traps and motion sensors, and spatial behavior was analyzed using activity reduction rates, migration distance, and path deviation indices. The results indicate a clear frequency-dependent response, with ultrasonic exposure at 30–35 kHz producing the strongest avoidance behavior and directional displacement. These findings suggest that ultrasonic deterrence primarily induces spatial displacement rather than population elimination and provide important implications for the development of adaptive ultrasonic–IoT systems to support smart and sustainable pest management in rice agriculture.
Adaptive Scheduling Model of Ultrasonic Frequencies Based on Environmental Data for Rice Field Rat Pest Control Sihotang, Hengki Tamando; A, Galih Prakoso Rizky; Sihotang, Jonhariono; Simbolon, Romasinta
International Journal of Enterprise Modelling Vol. 19 No. 3 (2025): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v19i3.164

Abstract

Rat infestation remains a major constraint to rice production, causing significant yield losses and threatening food security in many rice-growing regions. Although ultrasonic deterrent systems have been promoted as an environmentally friendly alternative to chemical rodenticides, their effectiveness is often inconsistent due to static frequency emission and rapid behavioral habituation. This study proposes an adaptive scheduling model for ultrasonic frequencies based on real-time environmental data to enhance long-term deterrence effectiveness. The model integrates environmental sensing, stochastic frequency selection, and habituation-aware control within a context-aware scheduling framework. Environmental data were acquired using field-deployed sensors, while the adaptive algorithm dynamically adjusted ultrasonic frequency, emission duration, and interval. Field evaluations compared the proposed system with static ultrasonic control. Results demonstrate sustained spectral diversity, reduced habituation, and significant decreases in rat activity and crop damage, alongside improved energy efficiency. These findings highlight the potential of adaptive ultrasonic control as a scalable and sustainable solution for smart agriculture, supporting chemical-free pest management and precision rice farming.
Co-Authors A, Galih Prakoso Rizky Achiriani, Tri Wahyuningtiyas Afrisawati Afrisawati Agustina Simangunsong Aisyah Alesha Alrasyid, Wildan Anthoni Anggrawan Anthony Anggrawan Bambang Saras Yulistiawan Bambang Saras Yulistiawan Bosker Sinaga Budi Arif Dermawan Calvin Berkat Iman Hulu Chandra, Suherman Dadang Pyanto Dahayu Annisa Nathania Dara Ramadhani Aresti Delano, Aldrich Desi Vinsensia Dini Anggraini Dwiki Rivaldo Naidu Efendi, Syahril Elpridawati Purba Endang Mistaorina Laia Erwin Panggabean Ezra Natasya.S Fadiel Rahmad Hidayat Firmansyah Firmansyah Fitri Andhika Fransisco alexander Simbolon Fristi Riandari Fristi Riandari Fristi Riandari Fristi Riandari Galih Prakoso Rizky A Galih Prakoso Rizky A Galih Prakoso Rizky A Galih Prakoso Rizky A Galih Prakoso Rizky A Galih Prakoso Rizky A. Guntur Syahputra Harapan Lumbantoruan Harapan Lumbantoruan Harpingka Fitria Br. Sibarani Harpingka Fitriai Br. Sibaran Hasugian , Paska Marto Henry Eko Hapsanto Herlina Zebua Herman Mawengkang Hikmah Adwin Adam 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 Jonson Manurung Judijanto, Loso Kharisma Wiati Gusti 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 Murni Marbun Normi Verawati Marbun Nurul Afifah Arifuddin Panjaitan, Firta Sari Patricius Michaud Felix Patrisia Teresa Marsoit Pilisman Buulolo Praffi Ramadhani Pujiastuti, Lise R. Fanry Siahaan R. Mahdalena Simanjorang Rasenda Rasenda Rasenda Rasenda Rifka Widyastuti Rifka Widyastuti Ririn Pebrina Br. Marpaung Rizky, Galih Prakoso Rohit Gautama Roma Sinta Simbolon Rosulastri Purba Rr Octanty Mulianingtyas RR Octanty Mulianingtyas Santiwati Sihotang Santoso, Heroe Satriyo Wibowo Sethu Ramen 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 Vinsensia, Desi Wildan Alrasyid Wildan Alrasyid Wildan Alrasyid Yudistira Alif Raditya